This document is an automatically generated cross reference for the ergm model terms from the stanet project. The source for this data and additional descriptions are in the ?ergm.terms
help file or the ergm manual.
It is possible to search the ergm-terms
help page and search for specific keywords of terms using the search.ergmTerms
command. For example to find all the terms that mention ‘triangle’ in their description:
## Found 9 matching ergm terms:
## Undir(formula, rule="weak")
## Evaluation on symmetrized (undirected) network
##
## localtriangle(x)
## Triangles within neighborhoods
##
## opentriad
## Open triads
##
## threepath
## Three-trails
##
## threetrail(keep=NULL, levels=NULL)
## Three-trails
##
## triangle(attr=NULL, diff=FALSE, levels=NULL)
## Triangles
##
## tripercent(attr=NULL, diff=FALSE, levels=NULL)
## Triangle percentage
##
## ttriple(attr=NULL, diff=FALSE, levels=NULL)
## Transitive triples
##
## ttriad
## Transitive triples
Or to find all of the dyad-independent bipartite terms:
## Found 16 matching ergm terms:
## b1cov(attr)
## Main effect of a covariate for the first mode in a bipartite network
##
## b1cov(attr, form="sum")
## Main effect of a covariate for the first mode in a bipartite network
##
## b1factor(attr, base=1, levels=-1)
## Factor attribute effect for the first mode in a bipartite network
##
## b1factor(attr, base=1, levels=-1, form="sum")
## Factor attribute effect for the first mode in a bipartite network
##
## b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL)
## Nodal attribute-based homophily effect for the first mode in a bipartite network
##
## b1sociality(nodes=-1)
## Degree
##
## b1sociality(nodes=-1, form="sum")
## Degree
##
## b2cov(attr)
## Main effect of a covariate for the second mode in a bipartite network
##
## b2cov(attr, form="sum")
## Main effect of a covariate for the second mode in a bipartite network
##
## b2factor(attr, base=1, levels=-1)
## Factor attribute effect for the second mode in a bipartite network
##
## b2factor(attr, base=1, levels=-1, form="sum")
## Factor attribute effect for the second mode in a bipartite network
##
## b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL)
## Nodal attribute-based homophily effect for the second mode in a bipartite network
##
## b2sociality(nodes=-1)
## Degree
##
## b2sociality(nodes=-1, form="sum")
## Degree
##
## diff(attr, pow=1, dir="t-h", sign.action="identity")
## Difference
##
## diff(attr, pow=1, dir="t-h", sign.action="identity", form ="sum")
## Difference
For convenience, this table lists a subset of the most commonly-used ergm terms and keywords.
Term bin val dir undir bip dyad-indep op
1 b1cov TRUE TRUE FALSE TRUE TRUE TRUE FALSE 2 b1degree TRUE FALSE FALSE TRUE TRUE FALSE FALSE 3 b1degreeL TRUE FALSE FALSE TRUE TRUE FALSE FALSE 4 b1factor TRUE TRUE FALSE TRUE TRUE TRUE FALSE 5 b1nodematch TRUE FALSE FALSE TRUE TRUE TRUE FALSE 6 b2concurrent TRUE FALSE FALSE TRUE TRUE FALSE FALSE 7 b2cov TRUE TRUE FALSE TRUE TRUE TRUE FALSE 8 b2degree TRUE FALSE FALSE TRUE TRUE FALSE FALSE 9 b2factor TRUE TRUE FALSE TRUE TRUE TRUE FALSE 10 b2nodematch TRUE FALSE FALSE TRUE TRUE TRUE FALSE 11 degree TRUE FALSE FALSE TRUE FALSE FALSE FALSE 12 degreeL TRUE FALSE TRUE TRUE FALSE FALSE FALSE 13 diff TRUE TRUE TRUE TRUE TRUE TRUE FALSE 14 edgecov TRUE TRUE TRUE TRUE FALSE TRUE FALSE 15 gwdegree TRUE FALSE FALSE TRUE FALSE FALSE FALSE 16 gwdegreeL TRUE FALSE FALSE TRUE FALSE FALSE FALSE 17 gwesp TRUE FALSE TRUE TRUE FALSE FALSE FALSE 18 idegree TRUE FALSE TRUE FALSE FALSE FALSE FALSE 19 idegreeL TRUE FALSE TRUE FALSE FALSE FALSE FALSE 20 isolates TRUE FALSE TRUE TRUE FALSE FALSE FALSE 21 mm TRUE TRUE TRUE TRUE FALSE TRUE FALSE 22 mutual TRUE TRUE TRUE FALSE FALSE FALSE FALSE 23 mutualL TRUE FALSE TRUE FALSE FALSE FALSE FALSE 24 nodecov TRUE TRUE TRUE TRUE FALSE TRUE FALSE 25 nodefactor TRUE TRUE TRUE TRUE FALSE TRUE FALSE 26 nodeicov TRUE TRUE TRUE FALSE FALSE FALSE FALSE 27 nodeifactor TRUE TRUE TRUE FALSE FALSE TRUE FALSE 28 nodematch TRUE TRUE TRUE TRUE FALSE TRUE FALSE 29 nodemix TRUE TRUE TRUE TRUE FALSE TRUE FALSE 30 odegree TRUE FALSE TRUE FALSE FALSE FALSE FALSE 31 odegreeL TRUE FALSE TRUE FALSE FALSE FALSE FALSE 32 triangle TRUE FALSE TRUE TRUE FALSE FALSE FALSE Link 1 b1cov-ergmTerm 2 b1degree-ergmTerm 3 b1degreeL-ergmTerm 4 b1factor-ergmTerm 5 b1nodematch-ergmTerm 6 b2concurrent-ergmTerm 7 b2cov-ergmTerm 8 b2degree-ergmTerm 9 b2factor-ergmTerm 10 b2nodematch-ergmTerm 11 degree-ergmTerm 12 degreeL-ergmTerm 13 diff-ergmTerm 14 edgecov-ergmTerm 15 gwdegree-ergmTerm 16 gwdegreeL-ergmTerm 17 gwesp-ergmTerm 18 idegree-ergmTerm 19 idegreeL-ergmTerm 20 isolates-ergmTerm 21 mm-ergmTerm 22 mutual-ergmTerm 23 mutualL-ergmTerm 24 nodecov-ergmTerm 25 nodefactor-ergmTerm 26 nodeicov-ergmTerm 27 nodeifactor-ergmTerm 28 nodematch-ergmTerm 29 nodemix-ergmTerm 30 odegree-ergmTerm 31 odegreeL-ergmTerm 32 triangle-ergmTerm
For convenience, this table lists operator terms: terms that wrap or modify other terms.
Term bin val dir undir bip dyad-indep op
1 B FALSE TRUE FALSE FALSE FALSE FALSE TRUE 2 Curve TRUE TRUE FALSE FALSE FALSE FALSE TRUE 3 Exp TRUE TRUE FALSE FALSE FALSE FALSE TRUE 4 F TRUE FALSE FALSE FALSE FALSE FALSE TRUE 5 L TRUE FALSE FALSE FALSE FALSE FALSE TRUE 6 Label TRUE TRUE FALSE FALSE FALSE FALSE TRUE 7 Log TRUE TRUE FALSE FALSE FALSE FALSE TRUE 8 N TRUE TRUE TRUE TRUE FALSE FALSE TRUE 9 NodematchFilter TRUE FALSE FALSE FALSE FALSE FALSE TRUE 10 Offset TRUE FALSE FALSE FALSE FALSE FALSE TRUE 11 Prod TRUE TRUE FALSE FALSE FALSE FALSE TRUE 12 S TRUE FALSE FALSE FALSE FALSE FALSE TRUE 13 Sum TRUE TRUE FALSE FALSE FALSE FALSE TRUE 14 Symmetrize TRUE FALSE TRUE FALSE FALSE FALSE TRUE Link 1 B-ergmTerm 2 Curve-ergmTerm 3 Exp-ergmTerm 4 F-ergmTerm 5 L-ergmTerm 6 Label-ergmTerm 7 Log-ergmTerm 8 N-ergmTerm 9 NodematchFilter-ergmTerm 10 Offset-ergmTerm 11 Prod-ergmTerm 12 S-ergmTerm 13 Sum-operator-ergmTerm 14 Symmetrize-ergmTerm
This table lists the complete set of terms available in the ergm package. In HTML versions, clicking on a term name will jump to its definition.
Term op val dir undir bin dyad-indep quant nodal attr
1 B TRUE TRUE FALSE FALSE FALSE FALSE FALSE 2 CMBL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 3 Curve TRUE TRUE FALSE FALSE FALSE TRUE FALSE 4 Exp TRUE TRUE FALSE FALSE FALSE TRUE FALSE 5 F TRUE FALSE FALSE FALSE FALSE TRUE FALSE 6 L TRUE FALSE FALSE TRUE FALSE TRUE FALSE 7 Label TRUE TRUE FALSE FALSE FALSE TRUE FALSE 8 Log TRUE TRUE FALSE FALSE FALSE TRUE FALSE 9 N TRUE TRUE TRUE FALSE TRUE TRUE FALSE 10 NodematchFilter TRUE FALSE FALSE FALSE FALSE TRUE FALSE 11 Offset TRUE FALSE FALSE FALSE FALSE TRUE FALSE 12 Prod TRUE TRUE FALSE FALSE FALSE TRUE FALSE 13 S TRUE FALSE FALSE FALSE FALSE TRUE FALSE 14 Sum TRUE TRUE FALSE FALSE FALSE TRUE FALSE 15 Symmetrize TRUE FALSE TRUE FALSE FALSE TRUE FALSE 16 absdiff FALSE TRUE TRUE FALSE TRUE TRUE TRUE 17 absdiffcat FALSE TRUE TRUE FALSE TRUE TRUE TRUE 18 altkstar FALSE FALSE FALSE FALSE TRUE TRUE FALSE 19 asymmetric FALSE FALSE TRUE FALSE FALSE TRUE TRUE 20 atleast FALSE TRUE TRUE FALSE TRUE FALSE TRUE 21 atmost FALSE TRUE TRUE FALSE TRUE FALSE TRUE 22 attrcov FALSE FALSE TRUE FALSE TRUE TRUE TRUE 23 b1concurrent FALSE FALSE FALSE FALSE TRUE TRUE FALSE 24 b1cov FALSE TRUE FALSE FALSE TRUE TRUE TRUE 25 b1degrange FALSE FALSE FALSE FALSE TRUE TRUE FALSE 26 b1degree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 27 b1degreeL FALSE FALSE FALSE FALSE TRUE TRUE FALSE 28 b1dsp FALSE FALSE FALSE FALSE TRUE TRUE FALSE 29 b1factor FALSE TRUE FALSE FALSE TRUE TRUE TRUE 30 b1mindegree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 31 b1nodematch FALSE FALSE FALSE FALSE TRUE TRUE TRUE 32 b1sociality FALSE TRUE FALSE FALSE TRUE TRUE TRUE 33 b1star FALSE FALSE FALSE FALSE TRUE TRUE FALSE 34 b1starmix FALSE FALSE FALSE FALSE TRUE TRUE FALSE 35 b1twostar FALSE FALSE FALSE FALSE TRUE TRUE FALSE 36 b2concurrent FALSE FALSE FALSE FALSE TRUE TRUE FALSE 37 b2cov FALSE TRUE FALSE FALSE TRUE TRUE TRUE 38 b2degrange FALSE FALSE FALSE FALSE TRUE TRUE FALSE 39 b2degree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 40 b2dsp FALSE FALSE FALSE FALSE TRUE TRUE FALSE 41 b2factor FALSE TRUE FALSE FALSE TRUE TRUE TRUE 42 b2mindegree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 43 b2nodematch FALSE FALSE FALSE FALSE TRUE TRUE TRUE 44 b2sociality FALSE TRUE FALSE FALSE TRUE TRUE TRUE 45 b2star FALSE FALSE FALSE FALSE TRUE TRUE FALSE 46 b2starmix FALSE FALSE FALSE FALSE TRUE TRUE FALSE 47 b2twostar FALSE FALSE FALSE FALSE TRUE TRUE FALSE 48 balance FALSE FALSE TRUE FALSE TRUE TRUE FALSE 49 coincidence FALSE FALSE FALSE FALSE TRUE TRUE FALSE 50 concurrent FALSE FALSE FALSE FALSE TRUE TRUE FALSE 51 concurrentties FALSE FALSE FALSE FALSE TRUE TRUE FALSE 52 ctriple FALSE FALSE TRUE FALSE FALSE TRUE FALSE 53 cycle FALSE FALSE TRUE FALSE TRUE TRUE FALSE 54 cyclicalties FALSE TRUE TRUE FALSE TRUE TRUE FALSE 55 cyclicalweights FALSE TRUE TRUE FALSE TRUE FALSE FALSE 56 ddsp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 57 ddspL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 58 degcor FALSE FALSE FALSE FALSE TRUE TRUE FALSE 59 degcrossprod FALSE FALSE FALSE FALSE TRUE TRUE FALSE 60 degrange FALSE FALSE FALSE FALSE TRUE TRUE FALSE 61 degree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 62 degree1.5 FALSE FALSE FALSE FALSE TRUE TRUE FALSE 63 degreeL FALSE FALSE TRUE FALSE TRUE TRUE FALSE 64 density FALSE FALSE TRUE FALSE TRUE TRUE TRUE 65 desp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 66 despL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 67 dgwdsp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 68 dgwdspL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 69 dgwesp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 70 dgwespL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 71 dgwnsp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 72 dgwnspL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 73 diff FALSE TRUE TRUE FALSE TRUE TRUE TRUE 74 dnsp FALSE FALSE TRUE FALSE FALSE TRUE FALSE 75 dnspL FALSE FALSE TRUE TRUE TRUE TRUE FALSE 76 dsp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 77 dyadcov FALSE FALSE TRUE FALSE TRUE TRUE TRUE 78 edgecov FALSE TRUE TRUE FALSE TRUE TRUE TRUE 79 edges FALSE TRUE TRUE FALSE TRUE TRUE TRUE 80 equalto FALSE TRUE TRUE FALSE TRUE FALSE TRUE 81 esp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 82 greaterthan FALSE TRUE TRUE FALSE TRUE FALSE TRUE 83 gwb1degree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 84 gwb1degreeL FALSE FALSE FALSE FALSE TRUE TRUE FALSE 85 gwb1dsp FALSE FALSE FALSE FALSE TRUE TRUE FALSE 86 gwb2degree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 87 gwb2degreeL FALSE FALSE FALSE FALSE TRUE TRUE FALSE 88 gwb2dsp FALSE FALSE FALSE FALSE TRUE TRUE FALSE 89 gwdegree FALSE FALSE FALSE FALSE TRUE TRUE FALSE 90 gwdegreeL FALSE FALSE FALSE FALSE TRUE TRUE FALSE 91 gwdsp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 92 gwesp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 93 gwidegree FALSE FALSE TRUE FALSE FALSE TRUE FALSE 94 gwidegreeL FALSE FALSE TRUE FALSE FALSE TRUE FALSE 95 gwnsp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 96 gwodegree FALSE FALSE TRUE FALSE FALSE TRUE FALSE 97 gwodegreeL FALSE FALSE TRUE FALSE FALSE TRUE FALSE 98 hamming FALSE FALSE TRUE FALSE TRUE TRUE TRUE 99 idegrange FALSE FALSE TRUE FALSE FALSE TRUE FALSE 100 idegree FALSE FALSE TRUE FALSE FALSE TRUE FALSE 101 idegree1.5 FALSE FALSE TRUE FALSE FALSE TRUE FALSE 102 idegreeL FALSE FALSE TRUE FALSE FALSE TRUE FALSE 103 ininterval FALSE TRUE TRUE FALSE TRUE FALSE TRUE 104 intransitive FALSE FALSE TRUE FALSE FALSE TRUE FALSE 105 isolatededges FALSE FALSE FALSE FALSE TRUE TRUE FALSE 106 isolates FALSE FALSE TRUE FALSE TRUE TRUE FALSE 107 istar FALSE FALSE TRUE FALSE FALSE TRUE FALSE 108 kstar FALSE FALSE FALSE FALSE TRUE TRUE FALSE 109 localtriangle FALSE FALSE TRUE FALSE TRUE TRUE FALSE 110 m2star FALSE FALSE TRUE FALSE FALSE TRUE FALSE 111 meandeg FALSE FALSE TRUE FALSE TRUE TRUE TRUE 112 mm FALSE TRUE TRUE FALSE TRUE TRUE TRUE 113 mutual FALSE TRUE TRUE FALSE FALSE TRUE FALSE 114 mutualL FALSE FALSE TRUE TRUE FALSE TRUE FALSE 115 nearsimmelian FALSE FALSE TRUE FALSE FALSE TRUE FALSE 116 nodecov FALSE TRUE TRUE FALSE TRUE TRUE TRUE 117 nodecovar FALSE TRUE TRUE FALSE FALSE FALSE FALSE 118 nodefactor FALSE TRUE TRUE FALSE TRUE TRUE TRUE 119 nodeicov FALSE TRUE TRUE FALSE FALSE TRUE FALSE 120 nodeicovar FALSE TRUE TRUE FALSE FALSE FALSE FALSE 121 nodeifactor FALSE TRUE TRUE FALSE FALSE TRUE TRUE 122 nodematch FALSE TRUE TRUE FALSE TRUE TRUE TRUE 123 nodemix FALSE TRUE TRUE FALSE TRUE TRUE TRUE 124 nodeocov FALSE TRUE TRUE FALSE FALSE TRUE TRUE 125 nodeocovar FALSE TRUE TRUE FALSE FALSE FALSE FALSE 126 nodeofactor FALSE TRUE TRUE FALSE FALSE TRUE TRUE 127 nsp FALSE FALSE TRUE FALSE TRUE TRUE FALSE 128 odegrange FALSE FALSE TRUE FALSE FALSE TRUE FALSE 129 odegree FALSE FALSE TRUE FALSE FALSE TRUE FALSE 130 odegree1.5 FALSE FALSE TRUE FALSE FALSE TRUE FALSE 131 odegreeL FALSE FALSE TRUE FALSE FALSE TRUE FALSE 132 opentriad FALSE FALSE FALSE FALSE TRUE TRUE FALSE 133 ostar FALSE FALSE TRUE FALSE FALSE TRUE FALSE 134 receiver FALSE TRUE TRUE FALSE FALSE TRUE TRUE 135 sender FALSE TRUE TRUE FALSE FALSE TRUE TRUE 136 simmelian FALSE FALSE TRUE FALSE FALSE TRUE FALSE 137 simmelianties FALSE FALSE TRUE FALSE FALSE TRUE FALSE 138 smalldiff FALSE FALSE TRUE FALSE TRUE TRUE TRUE 139 smallerthan FALSE TRUE TRUE FALSE TRUE FALSE TRUE 140 sociality FALSE TRUE FALSE FALSE TRUE TRUE TRUE 141 sum FALSE TRUE TRUE FALSE TRUE FALSE FALSE 142 threetrail FALSE FALSE TRUE FALSE TRUE TRUE FALSE 143 transitive FALSE FALSE TRUE FALSE FALSE TRUE FALSE 144 transitiveties FALSE FALSE TRUE FALSE TRUE TRUE FALSE 145 transitiveweights FALSE TRUE TRUE FALSE TRUE FALSE FALSE 146 triadcensus FALSE FALSE TRUE FALSE TRUE TRUE FALSE 147 triangle FALSE FALSE TRUE FALSE TRUE TRUE FALSE 148 tripercent FALSE FALSE FALSE FALSE TRUE TRUE FALSE 149 ttriple FALSE FALSE TRUE FALSE FALSE TRUE FALSE 150 twopath FALSE FALSE TRUE FALSE TRUE TRUE FALSE 151 twostarL FALSE FALSE TRUE TRUE TRUE TRUE FALSE cat nodal attr curved triad rel bip freq non-neg 1 FALSE FALSE FALSE FALSE FALSE FALSE 2 FALSE FALSE FALSE FALSE FALSE FALSE 3 FALSE FALSE FALSE FALSE FALSE FALSE 4 FALSE FALSE FALSE FALSE FALSE FALSE 5 FALSE FALSE FALSE FALSE FALSE FALSE 6 FALSE FALSE FALSE FALSE FALSE FALSE 7 FALSE FALSE FALSE FALSE FALSE FALSE 8 FALSE FALSE FALSE FALSE FALSE FALSE 9 FALSE FALSE FALSE FALSE FALSE FALSE 10 FALSE FALSE FALSE FALSE FALSE FALSE 11 FALSE FALSE FALSE FALSE FALSE FALSE 12 FALSE FALSE FALSE FALSE FALSE FALSE 13 FALSE FALSE FALSE FALSE FALSE FALSE 14 FALSE FALSE FALSE FALSE FALSE FALSE 15 FALSE FALSE FALSE FALSE FALSE FALSE 16 TRUE FALSE FALSE FALSE FALSE FALSE 17 FALSE TRUE FALSE FALSE FALSE FALSE 18 FALSE TRUE TRUE FALSE FALSE FALSE 19 FALSE FALSE FALSE TRUE FALSE FALSE 20 FALSE FALSE FALSE FALSE FALSE FALSE 21 FALSE FALSE FALSE FALSE FALSE FALSE 22 FALSE FALSE FALSE FALSE FALSE FALSE 23 FALSE TRUE FALSE FALSE TRUE FALSE 24 TRUE FALSE FALSE FALSE TRUE TRUE 25 FALSE FALSE FALSE FALSE TRUE FALSE 26 FALSE TRUE FALSE FALSE TRUE TRUE 27 FALSE TRUE FALSE FALSE TRUE TRUE 28 FALSE FALSE FALSE FALSE TRUE FALSE 29 FALSE TRUE FALSE FALSE TRUE TRUE 30 FALSE FALSE FALSE FALSE TRUE FALSE 31 FALSE TRUE FALSE FALSE TRUE TRUE 32 FALSE FALSE FALSE FALSE TRUE FALSE 33 FALSE TRUE FALSE FALSE TRUE FALSE 34 FALSE TRUE FALSE FALSE TRUE FALSE 35 FALSE TRUE FALSE FALSE TRUE FALSE 36 FALSE FALSE FALSE FALSE TRUE TRUE 37 TRUE FALSE FALSE FALSE TRUE TRUE 38 FALSE FALSE FALSE FALSE TRUE FALSE 39 FALSE TRUE FALSE FALSE TRUE TRUE 40 FALSE FALSE FALSE FALSE TRUE FALSE 41 FALSE TRUE FALSE FALSE TRUE TRUE 42 FALSE FALSE FALSE FALSE TRUE FALSE 43 FALSE TRUE FALSE FALSE TRUE TRUE 44 FALSE FALSE FALSE FALSE TRUE FALSE 45 FALSE TRUE FALSE FALSE TRUE FALSE 46 FALSE TRUE FALSE FALSE TRUE FALSE 47 FALSE TRUE FALSE FALSE TRUE FALSE 48 FALSE FALSE FALSE TRUE FALSE FALSE 49 FALSE FALSE FALSE FALSE TRUE FALSE 50 FALSE TRUE FALSE FALSE FALSE FALSE 51 FALSE TRUE FALSE FALSE FALSE FALSE 52 FALSE TRUE FALSE TRUE FALSE FALSE 53 FALSE FALSE FALSE FALSE FALSE FALSE 54 FALSE FALSE FALSE FALSE FALSE FALSE 55 FALSE FALSE FALSE FALSE FALSE FALSE 56 FALSE FALSE FALSE FALSE FALSE FALSE 57 FALSE FALSE FALSE FALSE FALSE FALSE 58 FALSE FALSE FALSE FALSE FALSE FALSE 59 FALSE FALSE FALSE FALSE FALSE FALSE 60 FALSE TRUE FALSE FALSE FALSE FALSE 61 FALSE TRUE FALSE FALSE FALSE TRUE 62 FALSE FALSE FALSE FALSE FALSE FALSE 63 FALSE TRUE FALSE FALSE FALSE TRUE 64 FALSE FALSE FALSE FALSE FALSE FALSE 65 FALSE FALSE FALSE FALSE FALSE FALSE 66 FALSE FALSE FALSE FALSE FALSE FALSE 67 FALSE FALSE FALSE FALSE FALSE FALSE 68 FALSE FALSE FALSE FALSE FALSE FALSE 69 FALSE FALSE FALSE FALSE FALSE FALSE 70 FALSE FALSE FALSE FALSE FALSE FALSE 71 FALSE FALSE FALSE FALSE FALSE FALSE 72 FALSE FALSE FALSE FALSE FALSE FALSE 73 TRUE FALSE FALSE FALSE TRUE TRUE 74 FALSE FALSE FALSE FALSE FALSE FALSE 75 FALSE FALSE FALSE FALSE FALSE FALSE 76 FALSE FALSE FALSE FALSE FALSE FALSE 77 FALSE TRUE FALSE FALSE FALSE FALSE 78 FALSE FALSE FALSE FALSE FALSE TRUE 79 FALSE FALSE FALSE FALSE FALSE FALSE 80 FALSE FALSE FALSE FALSE FALSE FALSE 81 FALSE FALSE FALSE FALSE FALSE FALSE 82 FALSE FALSE FALSE FALSE FALSE FALSE 83 FALSE FALSE TRUE FALSE TRUE FALSE 84 FALSE FALSE TRUE FALSE TRUE FALSE 85 FALSE FALSE TRUE FALSE TRUE FALSE 86 FALSE FALSE TRUE FALSE TRUE FALSE 87 FALSE FALSE TRUE FALSE TRUE FALSE 88 FALSE FALSE TRUE FALSE TRUE FALSE 89 FALSE FALSE TRUE FALSE FALSE TRUE 90 FALSE FALSE TRUE FALSE FALSE TRUE 91 FALSE FALSE TRUE FALSE FALSE FALSE 92 FALSE FALSE TRUE FALSE FALSE TRUE 93 FALSE FALSE TRUE FALSE FALSE FALSE 94 FALSE FALSE TRUE FALSE FALSE FALSE 95 FALSE FALSE TRUE FALSE FALSE FALSE 96 FALSE FALSE TRUE FALSE FALSE FALSE 97 FALSE FALSE TRUE FALSE FALSE FALSE 98 FALSE FALSE FALSE FALSE FALSE FALSE 99 FALSE TRUE FALSE FALSE FALSE FALSE 100 FALSE TRUE FALSE FALSE FALSE TRUE 101 FALSE FALSE FALSE FALSE FALSE FALSE 102 FALSE TRUE FALSE FALSE FALSE TRUE 103 FALSE FALSE FALSE FALSE FALSE FALSE 104 FALSE FALSE FALSE TRUE FALSE FALSE 105 FALSE FALSE FALSE FALSE TRUE FALSE 106 FALSE FALSE FALSE FALSE FALSE TRUE 107 FALSE TRUE FALSE FALSE FALSE FALSE 108 FALSE TRUE FALSE FALSE FALSE FALSE 109 FALSE FALSE FALSE TRUE FALSE FALSE 110 FALSE FALSE FALSE FALSE FALSE FALSE 111 FALSE FALSE FALSE FALSE FALSE FALSE 112 FALSE TRUE FALSE FALSE FALSE TRUE 113 FALSE FALSE FALSE FALSE FALSE TRUE 114 FALSE FALSE FALSE FALSE FALSE TRUE 115 FALSE FALSE FALSE TRUE FALSE FALSE 116 TRUE FALSE FALSE FALSE FALSE TRUE 117 FALSE FALSE FALSE FALSE FALSE FALSE 118 FALSE TRUE FALSE FALSE FALSE TRUE 119 TRUE FALSE FALSE FALSE FALSE TRUE 120 FALSE FALSE FALSE FALSE FALSE FALSE 121 FALSE TRUE FALSE FALSE FALSE TRUE 122 FALSE TRUE FALSE FALSE FALSE TRUE 123 FALSE TRUE FALSE FALSE FALSE TRUE 124 TRUE FALSE FALSE FALSE FALSE FALSE 125 FALSE FALSE FALSE FALSE FALSE FALSE 126 FALSE TRUE FALSE FALSE FALSE FALSE 127 FALSE FALSE FALSE FALSE FALSE FALSE 128 FALSE TRUE FALSE FALSE FALSE FALSE 129 FALSE TRUE FALSE FALSE FALSE TRUE 130 FALSE FALSE FALSE FALSE FALSE FALSE 131 FALSE TRUE FALSE FALSE FALSE TRUE 132 FALSE FALSE FALSE TRUE FALSE FALSE 133 FALSE TRUE FALSE FALSE FALSE FALSE 134 FALSE FALSE FALSE FALSE FALSE FALSE 135 FALSE FALSE FALSE FALSE FALSE FALSE 136 FALSE FALSE FALSE TRUE FALSE FALSE 137 FALSE FALSE FALSE TRUE FALSE FALSE 138 TRUE FALSE FALSE FALSE FALSE FALSE 139 FALSE FALSE FALSE FALSE FALSE FALSE 140 FALSE TRUE FALSE FALSE FALSE FALSE 141 FALSE FALSE FALSE FALSE FALSE FALSE 142 FALSE FALSE FALSE TRUE FALSE FALSE 143 FALSE FALSE FALSE TRUE FALSE FALSE 144 FALSE TRUE FALSE TRUE FALSE FALSE 145 FALSE FALSE FALSE TRUE FALSE FALSE 146 FALSE FALSE FALSE TRUE FALSE FALSE 147 FALSE TRUE FALSE TRUE FALSE TRUE 148 FALSE TRUE FALSE TRUE FALSE FALSE 149 FALSE TRUE FALSE TRUE FALSE FALSE 150 FALSE FALSE FALSE FALSE FALSE FALSE 151 FALSE FALSE FALSE FALSE FALSE FALSE Link 1 B-ergmTerm 2 CMBL-ergmTerm 3 Curve-ergmTerm 4 Exp-ergmTerm 5 F-ergmTerm 6 L-ergmTerm 7 Label-ergmTerm 8 Log-ergmTerm 9 N-ergmTerm 10 NodematchFilter-ergmTerm 11 Offset-ergmTerm 12 Prod-ergmTerm 13 S-ergmTerm 14 Sum-operator-ergmTerm 15 Symmetrize-ergmTerm 16 absdiff-ergmTerm 17 absdiffcat-ergmTerm 18 altkstar-ergmTerm 19 asymmetric-ergmTerm 20 atleast-ergmTerm 21 atmost-ergmTerm 22 attrcov-ergmTerm 23 b1concurrent-ergmTerm 24 b1cov-ergmTerm 25 b1degrange-ergmTerm 26 b1degree-ergmTerm 27 b1degreeL-ergmTerm 28 b1dsp-ergmTerm 29 b1factor-ergmTerm 30 b1mindegree-ergmTerm 31 b1nodematch-ergmTerm 32 b1sociality-ergmTerm 33 b1star-ergmTerm 34 b1starmix-ergmTerm 35 b1twostar-ergmTerm 36 b2concurrent-ergmTerm 37 b2cov-ergmTerm 38 b2degrange-ergmTerm 39 b2degree-ergmTerm 40 b2dsp-ergmTerm 41 b2factor-ergmTerm 42 b2mindegree-ergmTerm 43 b2nodematch-ergmTerm 44 b2sociality-ergmTerm 45 b2star-ergmTerm 46 b2starmix-ergmTerm 47 b2twostar-ergmTerm 48 balance-ergmTerm 49 coincidence-ergmTerm 50 concurrent-ergmTerm 51 concurrentties-ergmTerm 52 ctriple-ergmTerm 53 cycle-ergmTerm 54 cyclicalties-ergmTerm 55 cyclicalweights-ergmTerm 56 ddsp-ergmTerm 57 ddspL-ergmTerm 58 degcor-ergmTerm 59 degcrossprod-ergmTerm 60 degrange-ergmTerm 61 degree-ergmTerm 62 degree1.5-ergmTerm 63 degreeL-ergmTerm 64 density-ergmTerm 65 desp-ergmTerm 66 despL-ergmTerm 67 dgwdsp-ergmTerm 68 dgwdspL-ergmTerm 69 dgwesp-ergmTerm 70 dgwespL-ergmTerm 71 dgwnsp-ergmTerm 72 dgwnspL-ergmTerm 73 diff-ergmTerm 74 dnsp-ergmTerm 75 dnspL-ergmTerm 76 dsp-ergmTerm 77 dyadcov-ergmTerm 78 edgecov-ergmTerm 79 edges-ergmTerm 80 equalto-ergmTerm 81 esp-ergmTerm 82 greaterthan-ergmTerm 83 gwb1degree-ergmTerm 84 gwb1degreeL-ergmTerm 85 gwb1dsp-ergmTerm 86 gwb2degree-ergmTerm 87 gwb2degreeL-ergmTerm 88 gwb2dsp-ergmTerm 89 gwdegree-ergmTerm 90 gwdegreeL-ergmTerm 91 gwdsp-ergmTerm 92 gwesp-ergmTerm 93 gwidegree-ergmTerm 94 gwidegreeL-ergmTerm 95 gwnsp-ergmTerm 96 gwodegree-ergmTerm 97 gwodegreeL-ergmTerm 98 hamming-ergmTerm 99 idegrange-ergmTerm 100 idegree-ergmTerm 101 idegree1.5-ergmTerm 102 idegreeL-ergmTerm 103 ininterval-ergmTerm 104 intransitive-ergmTerm 105 isolatededges-ergmTerm 106 isolates-ergmTerm 107 istar-ergmTerm 108 kstar-ergmTerm 109 localtriangle-ergmTerm 110 m2star-ergmTerm 111 meandeg-ergmTerm 112 mm-ergmTerm 113 mutual-ergmTerm 114 mutualL-ergmTerm 115 nearsimmelian-ergmTerm 116 nodecov-ergmTerm 117 nodecovar-ergmTerm 118 nodefactor-ergmTerm 119 nodeicov-ergmTerm 120 nodeicovar-ergmTerm 121 nodeifactor-ergmTerm 122 nodematch-ergmTerm 123 nodemix-ergmTerm 124 nodeocov-ergmTerm 125 nodeocovar-ergmTerm 126 nodeofactor-ergmTerm 127 nsp-ergmTerm 128 odegrange-ergmTerm 129 odegree-ergmTerm 130 odegree1.5-ergmTerm 131 odegreeL-ergmTerm 132 opentriad-ergmTerm 133 ostar-ergmTerm 134 receiver-ergmTerm 135 sender-ergmTerm 136 simmelian-ergmTerm 137 simmelianties-ergmTerm 138 smalldiff-ergmTerm 139 smallerthan-ergmTerm 140 sociality-ergmTerm 141 sum-ergmTerm 142 threetrail-ergmTerm 143 transitive-ergmTerm 144 transitiveties-ergmTerm 145 transitiveweights-ergmTerm 146 triadcensus-ergmTerm 147 triangle-ergmTerm 148 tripercent-ergmTerm 149 ttriple-ergmTerm 150 twopath-ergmTerm 151 twostarL-ergmTerm
This table lists full definitions for all of the terms along with their tags. Note that some terms may have multiple versions (e.g. valued vs. binary) with slightly different arguments and will be listed more than once with the same definition.
Description | Categories |
---|---|
B(formula, form) For example, B(~nodecov(“a”), form=“sum”) is equivalent to nodecov(“a”, form=“sum”) and similarly with form=“nonzero” . When a valued implementation is available, it should be preferred, as it is likely to be faster. |
operator, valued |
CMBL(Ls=~.) Conway–Maxwell-Binomial dependence among layers: A positive coefficient induces positive dependence and a negative one induces negative dependence. |
directed, layer-aware, undirected, binary |
Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) Curve(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) If the model in formula is curved, then the outputs of this operator term’s map argument will be used as inputs to the curved terms of the formula model. Curve is an obsolete alias and may be deprecated and removed in a future release. |
operator, binary, valued |
Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) Parametrise(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) If the model in formula is curved, then the outputs of this operator term’s map argument will be used as inputs to the curved terms of the formula model. Curve is an obsolete alias and may be deprecated and removed in a future release. |
operator, binary, valued |
Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) Parametrize(formula, params, map, gradient=NULL, minpar=-Inf, maxpar=+Inf, cov=NULL) If the model in formula is curved, then the outputs of this operator term’s map argument will be used as inputs to the curved terms of the formula model. Curve is an obsolete alias and may be deprecated and removed in a future release. |
operator, binary, valued |
Exp(formula) Exp(formula) Exponentiate a network’s statistic: Evaluate the terms specified in formula and exponentiates them with base e . |
operator, binary, valued |
F(formula, filter) Filtering on arbitrary one-term model.: Evaluates the given formula on a network constructed by taking y and removing any edges for which f_{i,j}(y_{i,j}) = 0f[i,j] (y[i,j])=0 . |
operator, binary |
L(formula, Ls=~.) Evaluation on layers: |
layer-aware, operator, binary |
Label(formula, label, pos) Label(formulas, label, pos) Modify terms’ coefficient names: This operator evaluates formula without modification, but modifies its coefficient and/or parameter names based on label and pos . |
operator, binary, valued |
Log(formula, log0=-1/sqrt(.Machine\(double.eps)) Log(formula, log0=-1/sqrt(.Machine\)double.eps)) Take a natural logarithm of a network’s statistic: Evaluate the terms specified in formula and takes a natural (base e ) logarithm of them. Since an ERGM statistic must be finite, log0 specifies the value to be substituted for log(0) . The default value seems reasonable for most purposes. |
operator, binary, valued |
N(formula, lm=~1, subset=TRUE, weights=1, contrasts=NULL, offset=0, label=NULL) N(formula, lm=~1, subset=TRUE, weights=1, contrasts=NULL, offset=0, label=NULL) Evaluation on multiple networks: |
directed, operator, undirected, binary, valued |
NodematchFilter(formula, attrname) Filtering on nodematch: Evaluates the terms specified in formula on a network constructed by taking y and removing any edges for which attrname(i)!=attrname(j) . |
operator, binary |
Offset(formula, coef, which) Terms with fixed coefficients: This operator is analogous to the offset() wrapper, but the coefficients are specified within the term and the curved ERGM mechanism is used internally. |
operator, binary |
Prod(formulas, label) Prod(formulas, label) If a formula has an LHS, it is interpreted as follows: a numeric scalar: Network statistics of this formula will be exponentiated by this. a numeric vector: Corresponding network statistics of this formula will be exponentiated by this. a numeric matrix: Vector of network statistics will be exponentiated by this using the same pattern as matrix multiplication. a character string: One of several predefined linear combinations. Currently supported presets are as follows: “prod”: Network statistics of this formula will be multiplied together; equivalent to matrix(1,1,p) , where p is the length of the network statistic vector. “geomean”: Network statistics of this formula will be geometrically averaged; equivalent to matrix(1/p,1,p) , where p is the length of the network statistic vector. Note that each formula must either produce the same number of statistics or be mapped through a matrix to produce the same number of statistics. A single formula is also permitted. This can be useful if one wishes to, say, scale or multiply together the statistics returned by a formula. Offsets are ignored unless there is only one formula and the transformation only scales the statistics (i.e., the effective transformation matrix is diagonal). Curved models are supported, subject to some limitations. In particular, the first model’s etamap will be used, overwriting the others. If label is not of length 1, it should have an attr -style attribute “curved” specifying the names for the curved parameters. |
operator, binary, valued |
S(formula, attrs) |
operator, binary |
Sum(formulas, label) Sum(formulas, label) If a formula has an LHS, it is interpreted as follows: a numeric scalar: Network statistics of this formula will be multiplied by this. a numeric vector: Corresponding network statistics of this formula will be multiplied by this. a numeric matrix: Vector of network statistics will be pre-multiplied by this. a character string: One of several predefined linear combinations. Currently supported presets are as follows: “sum” Network statistics of this formula will be summed up; equivalent to matrix(1,1,p) , where p is the length of the network statistic vector. “mean” Network statistics of this formula will be averaged; equivalent to matrix(1/p,1,p) , where p is the length of the network statistic vector. Note that each formula must either produce the same number of statistics or be mapped through a matrix to produce the same number of statistics. A single formula is also permitted. This can be useful if one wishes to, say, scale or sum up the statistics returned by a formula. Offsets are ignored unless there is only one formula and the transformation only scales the statistics (i.e., the effective transformation matrix is diagonal). Curved models are supported, subject to some limitations. In particular, the first model’s etamap will be used, overwriting the others. If label is not of length 1, it should have an attr -style attribute “curved” specifying the names for the curved parameters. |
operator, binary, valued |
Undir(formula, rule=“weak”) |
directed, operator, binary |
absdiff(attr, pow=1) absdiff(attr, pow=1, form=“sum”) Absolute difference: This term adds one network statistic to the model equaling the sum of abs(attr[i]-attr[j])^pow for all edges (i,j) in the network. |
directed, dyad-independent, quantitative nodal attribute, undirected, binary, valued |
absdiffcat(attr, base=NULL, levels=NULL) absdiffcat(attr, base=NULL, levels=NULL, form=“sum”) Categorical absolute difference: This term adds one statistic for every possible nonzero distinct value of abs(attr[i]-attr[j]) in the network. The value of each such statistic is the number of edges in the network with the corresponding absolute difference. |
categorical nodal attribute, directed, dyad-independent, undirected, binary, valued |
altkstar(lambda, fixed=FALSE) Alternating k-star: This is the version given in Snijders et al. (2006). The gwdegree and altkstar produce mathematically equivalent models, as long as they are used together with the edges (or kstar(1)) term, yet the interpretation of the gwdegree parameters is slightly more straightforward than the interpretation of the altkstar parameters. For this reason, we recommend the use of the gwdegree instead of altkstar. See Section 3 and especially equation (13) of Hunter (2007) for details. |
categorical nodal attribute, curved, undirected, binary |
asymmetric(attr=NULL, diff=FALSE, keep=NULL, levels=NULL) Asymmetric dyads: This term adds one network statistic to the model equal to the number of pairs of actors for which exactly one of (i{}j)(i,j) or (j{}i)(j,i) exists. |
directed, dyad-independent, triad-related, binary |
atleast(threshold=0) Number of dyads with values greater than or equal to a threshold: Adds the number of statistics equal to the length of threshold equaling to the number of dyads whose values equal or exceed the corresponding element of threshold . |
directed, dyad-independent, undirected, valued |
atmost(threshold=0) Number of dyads with values less than or equal to a threshold: Adds the number of statistics equal to the length of threshold equaling to the number of dyads whose values equal or are exceeded by the corresponding element of threshold . |
directed, dyad-independent, undirected, valued |
attrcov(attr, mat) |
directed, dyad-independent, undirected, binary |
b1concurrent(by=NULL, levels=NULL) Concurrent node count for the first mode in a bipartite network: This term adds one network statistic to the model, equal to the number of nodes in the first mode of the network with degree 2 or higher. The first mode of a bipartite network object is sometimes known as the “actor” mode. This term can only be used with undirected bipartite networks. |
bipartite, categorical nodal attribute, undirected, binary |
b1cov(attr) b1cov(attr, form=“sum”) Main effect of a covariate for the first mode in a bipartite network: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the total value of attr(i) for all edges /eqn(i,j) in the network. This term may only be used with bipartite networks. For categorical attributes, see b1factor . |
bipartite, dyad-independent, frequently-used, quantitative nodal attribute, undirected, binary, valued |
b1degrange(from, to= |
bipartite, undirected, binary |
b1degree(d, by=NULL, levels=NULL) Degree for the first mode in a bipartite network: This term adds one network statistic to the model for each element in d ; the ith such statistic equals the number of nodes of degree d[i] in the first mode of a bipartite network, i.e. with exactly d[i] edges. The first mode of a bipartite network object is sometimes known as the “actor” mode. |
bipartite, categorical nodal attribute, frequently-used, undirected, binary |
b1degreeL(d, by=NULL, levels=NULL, Ls=NULL) Degree for the first mode in a bipartite (aka two-mode) network: |
bipartite, categorical nodal attribute, frequently-used, undirected, binary |
b1dsp(d) Dyadwise shared partners for dyads in the first bipartition: This term adds one network statistic to the model for each element in d ; the ith such statistic equals the number of dyads in the first bipartition with exactly d[i] shared partners. (Those shared partners, of course, must be members of the second bipartition.) This term can only be used with bipartite networks. |
bipartite, undirected, binary |
b1factor(attr, base=1, levels=-1) b1factor(attr, base=1, levels=-1, form=“sum”) Factor attribute effect for the first mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the unique values of the attr attribute. Each of these statistics gives the number of times a node with that attribute in the first mode of the network appears in an edge. The first mode of a bipartite network object is sometimes known as the “actor” mode. |
bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued |
b1mindegree(d) Minimum degree for the first mode in a bipartite network: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of nodes in the first mode of a bipartite network with at least degree d[i] . The first mode of a bipartite network object is sometimes known as the “actor” mode. |
bipartite, undirected, binary |
b1nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb2attr=NULL, levels=NULL) |
bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary |
b1sociality(nodes=-1) b1sociality(nodes=-1, form=“sum”) Degree: This term adds one network statistic for each node in the first bipartition, equal to the number of ties of that node. This term can only be used with bipartite networks. For directed networks, see sender and receiver. For unipartite networks, see sociality. |
bipartite, dyad-independent, undirected, binary, valued |
b1star(k, attr=NULL, levels=NULL) k-Stars for the first mode in a bipartite network: This term adds one network statistic to the model for each element in k . The i th such statistic counts the number of distinct k[i] -stars whose center node is in the first mode of the network. The first mode of a bipartite network object is sometimes known as the “actor” mode. A k -star is defined to be a center node N and a set of k different nodes {O_1, , O_k}{O[1], …, O[k]} such that the ties {N, O_i}{N, O[i]} exist for i=1, , k. If args is specified then the count is over the number of k-stars (with center node in the first mode) where all nodes have the same value of the attribute. This term can only be used for undirected bipartite networks. |
bipartite, categorical nodal attribute, undirected, binary |
b1starmix(k, attr, base=NULL, diff=TRUE) Mixing matrix for k-stars centered on the first mode of a bipartite network: This term counts all k-stars in which the b2 nodes (called events in some contexts) are homophilous in the sense that they all share the same value of attr . However, the b1 node (in some contexts, the actor) at the center of the k-star does NOT have to have the same value as the b2 nodes; indeed, the values taken by the b1 nodes may be completely distinct from those of the b2 nodes, which allows for the use of this term in cases where there are two separate nodal attributes, one for the b1 nodes and another for the b2 nodes (in this case, however, these two attributes should be combined to form a single nodal attribute, attr ). A different statistic is created for each value of attr seen in a b1 node, even if no k-stars are observed with this value. |
bipartite, categorical nodal attribute, undirected, binary |
b1twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) Two-star census for central nodes centered on the first mode of a bipartite network: This term takes two nodal attributes. Assuming that there are n_1 values of b1attr among the b1 nodes and n_2 values of b2attr among the b2 nodes, then the total number of distinct categories of two stars according to these two attributes is n_1(n_2)(n_2+1)/2. By default, this model term creates a distinct statistic counting each of these categories. |
bipartite, categorical nodal attribute, undirected, binary |
b2concurrent(by=NULL) Concurrent node count for the second mode in a bipartite network: This term adds one network statistic to the model, equal to the number of nodes in the second mode of the network with degree 2 or higher. The second mode of a bipartite network object is sometimes known as the “event” mode. Without the optional argument, this statistic is equivalent to b2mindegree(2). |
bipartite, frequently-used, undirected, binary |
b2cov(attr) b2cov(attr, form=“sum”) Main effect of a covariate for the second mode in a bipartite network: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the total value of attr(j) for all edges (i,j) in the network. This term may only be used with bipartite networks. For categorical attributes, see b2factor. |
bipartite, dyad-independent, frequently-used, quantitative nodal attribute, undirected, binary, valued |
b2degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) |
bipartite, undirected, binary |
b2degree(d, by=NULL) Degree for the second mode in a bipartite (aka two-mode) network: |
bipartite, categorical nodal attribute, frequently-used, undirected, binary |
b2dsp(d) Dyadwise shared partners for dyads in the second bipartition: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of dyads in the second bipartition with exactly d[i] shared partners. (Those shared partners, of course, must be members of the first bipartition.) This term can only be used with bipartite networks. |
bipartite, undirected, binary |
b2factor(attr, base=1, levels=-1) b2factor(attr, base=1, levels=-1, form=“sum”) Factor attribute effect for the second mode in a bipartite network: This term adds multiple network statistics to the model, one for each of (a subset of) the unique values of the attr attribute. Each of these statistics gives the number of times a node with that attribute in the second mode of the network appears in an edge. The second mode of a bipartite network object is sometimes known as the “event” mode. |
bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary, valued |
b2mindegree(d) Minimum degree for the second mode in a bipartite network: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of nodes in the second mode of a bipartite network with at least degree d[i] . The second mode of a bipartite network object is sometimes known as the “event” mode. |
bipartite, undirected, binary |
b2nodematch(attr, diff=FALSE, keep=NULL, alpha=1, beta=1, byb1attr=NULL, levels=NULL) |
bipartite, categorical nodal attribute, dyad-independent, frequently-used, undirected, binary |
b2sociality(nodes=-1) b2sociality(nodes=-1, form=“sum”) Degree: This term adds one network statistic for each node in the second bipartition, equal to the number of ties of that node. For directed networks, see sender and receiver . For unipartite networks, see sociality . |
bipartite, dyad-independent, undirected, binary, valued |
b2star(k, attr=NULL, levels=NULL) k-Stars for the second mode in a bipartite network: This term adds one network statistic to the model for each element in k . The i th such statistic counts the number of distinct k[i] -stars whose center node is in the second mode of the network. The second mode of a bipartite network object is sometimes known as the “event” mode. A k -star is defined to be a center node N and a set of k different nodes {O_1, , O_k}{O[1], …, O[k]} such that the ties {N, O_i} exist for i=1, , k . This term can only be used for undirected bipartite networks. |
bipartite, categorical nodal attribute, undirected, binary |
b2starmix(k, attr, base=NULL, diff=TRUE) Mixing matrix for k-stars centered on the second mode of a bipartite network: This term is exactly the same as b1starmix except that the roles of b1 and b2 are reversed. |
bipartite, categorical nodal attribute, undirected, binary |
b2twostar(b1attr, b2attr, base=NULL, b1levels=NULL, b2levels=NULL, levels2=NULL) Two-star census for central nodes centered on the second mode of a bipartite network: This term is exactly the same as b1twostar except that the roles of b1 and b2 are reversed. |
bipartite, categorical nodal attribute, undirected, binary |
balance Balanced triads: This term adds one network statistic to the model equal to the number of triads in the network that are balanced. The balanced triads are those of type 102 or 300 in the categorization of Davis and Leinhardt (1972). For details on the 16 possible triad types, see ?triad.classify in the {sna} package. For an undirected network, the balanced triads are those with an odd number of ties (i.e., 1 and 3). |
directed, triad-related, undirected, binary |
coincidence(levels=NULL,active=0) Coincident node count for the second mode in a bipartite (aka two-mode) network: By default this term adds one network statistic to the model for each pair of nodes of mode two. It is equal to the number of (first mode) mutual partners of that pair. The first mode of a bipartite network object is sometimes known as the “actor” mode and the seconds as the “event” mode. So this is the number of actors going to both events in the pair. This term can only be used with undirected bipartite networks. |
bipartite, undirected, binary |
concurrent(by=NULL, levels=NULL) Concurrent node count: This term adds one network statistic to the model, equal to the number of nodes in the network with degree 2 or higher. This term can only be used with undirected networks. |
categorical nodal attribute, undirected, binary |
concurrentties(by=NULL, levels=NULL) Concurrent tie count: This term adds one network statistic to the model, equal to the number of ties incident on each actor beyond the first. This term can only be used with undirected networks. |
categorical nodal attribute, undirected, binary |
ctriple(attr=NULL, diff=FALSE, levels=NULL) Cyclic triples: By default, this term adds one statistic to the model, equal to the number of cyclic triples in the network, defined as a set of edges of the form {(i{}j), (j{}k), (k{}i)}{(i,j), (j,k), (k,i)} . |
categorical nodal attribute, directed, triad-related, binary |
ctriad Cyclic triples: By default, this term adds one statistic to the model, equal to the number of cyclic triples in the network, defined as a set of edges of the form {(i{}j), (j{}k), (k{}i)}{(i,j), (j,k), (k,i)} . |
categorical nodal attribute, directed, triad-related, binary |
cycle(k, semi=FALSE) |
directed, undirected, binary |
cyclicalties(attr=NULL, levels=NULL) cyclicalties(threshold=0) Cyclical ties: This term adds one statistic, equal to the number of ties iji–>j such that there exists a two-path from j to i . (Related to the ttriple term.) |
directed, undirected, binary, valued |
cyclicalweights(twopath=“min”, combine=“max”, affect=“min”) Cyclical weights: This statistic implements the cyclical weights statistic, like that defined by Krivitsky (2012), Equation 13, but with the focus dyad being y_{j,i} rather than y_{i,j} . For each option, the first (and the default) is more stable but also more conservative, while the second is more sensitive but more likely to induce a multimodal distribution of networks. |
directed, non-negative, undirected, valued |
ddsp(d, type=“OTP”) Directed dyadwise shared partners: This term adds one network statistic to the model for each element in d where the i th such statistic equals the number of dyads in the network with exactly d[i] shared partners. |
directed, binary |
ddspL(d, type=“OTP”, Ls.path=NULL, L.in_order=FALSE) Dyadwise shared partners on layers: |
directed, layer-aware, undirected, binary |
dspL(d, type=“OTP”, Ls.path=NULL, L.in_order=FALSE) Dyadwise shared partners on layers: |
directed, layer-aware, undirected, binary |
degcor Degree Correlation: This term adds one network statistic equal to the correlation of the degrees of all pairs of nodes in the network which are tied. Only coded for undirected networks. |
undirected, binary |
degcrossprod Degree Cross-Product: This term adds one network statistic equal to the mean of the cross-products of the degrees of all pairs of nodes in the network which are tied. Only coded for undirected networks. |
undirected, binary |
degrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) |
categorical nodal attribute, undirected, binary |
degree(d, by=NULL, homophily=FALSE, levels=NULL) Degree: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of nodes in the network of degree d[i] , i.e. with exactly d[i] edges. This term can only be used with undirected networks; for directed networks see idegree and odegree . |
categorical nodal attribute, frequently-used, undirected, binary |
degree1.5 Degree to the 3/2 power: This term adds one network statistic to the model equaling the sum over the actors of each actor’s degree taken to the 3/2 power (or, equivalently, multiplied by its square root). This term is an undirected analog to the terms of Snijders et al. (2010), equations (11) and (12). This term can only be used with undirected networks. |
undirected, binary |
degreeL(d, by=NULL, homophily=FALSE, levels=NULL, Ls=NULL) Degree: |
categorical nodal attribute, directed, frequently-used, undirected, binary |
density Density: This term adds one network statistic equal to the density of the network. For undirected networks, density equals kstar(1) or edges divided by n(n-1)/2 ; for directed networks, density equals edges or istar(1) or ostar(1) divided by n(n-1) . |
directed, dyad-independent, undirected, binary |
desp(d, type=“OTP”) Directed edgewise shared partners: This term adds one network statistic to the model for each element in d where the i th such statistic equals the number of edges in the network with exactly d[i] shared partners. |
directed, binary |
despL(d, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Edgewise shared partners on layers: |
directed, layer-aware, undirected, binary |
espL(d, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Edgewise shared partners on layers: |
directed, layer-aware, undirected, binary |
dgwdsp(decay, fixed=FALSE, cutoff=30, type=“OTP”) Geometrically weighted dyadwise shared partner distribution: This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared partner distribution with decay parameter decay parameter. |
directed, binary |
dgwdspL(decay, fixed=FALSE, cutoff=30, type=“OTP”, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted dyadwise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
gwdspL(decay, fixed=FALSE, cutoff=30, type=“OTP”, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted dyadwise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
dgwesp(decay, fixed=FALSE, cutoff=30, type=“OTP”) Geometrically weighted edgewise shared partner distribution: This term adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared partner distribution with decay parameter decay parameter. |
directed, binary |
dgwespL(decay, fixed=FALSE, cutoff=30, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted edgewise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
gwespL(decay, fixed=FALSE, cutoff=30, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted edgewise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
dgwnsp(decay, fixed=FALSE, cutoff=30, type=“OTP”) Geometrically weighted non-edgewise shared partner distribution: This term is just like gwesp and gwdsp except it adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared partner distribution with decay parameter decay parameter. |
directed, binary |
dgwnspL(decay, fixed=FALSE, cutoff=30, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted non-edgewise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
gwnspL(decay, fixed=FALSE, cutoff=30, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Geometrically weighted non-edgewise shared partner distribution on layers: |
directed, layer-aware, undirected, binary |
diff(attr, pow=1, dir=“t-h”, sign.action=“identity”) diff(attr, pow=1, dir=“t-h”, sign.action=“identity”, form =“sum”) |
bipartite, directed, dyad-independent, frequently-used, quantitative nodal attribute, undirected, binary, valued |
dnsp(d, type=“OTP”) Directed non-edgewise shared partners: This term adds one network statistic to the model for each element in d where the i th such statistic equals the number of non-edges in the network with exactly d[i] shared partners. |
directed, binary |
dnspL(d, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Non-edgewise shared partners and paths on layers: |
directed, layer-aware, undirected, binary |
nspL(d, type=“OTP”, L.base=NULL, Ls.path=NULL, L.in_order=FALSE) Non-edgewise shared partners and paths on layers: |
directed, layer-aware, undirected, binary |
dsp(d) Dyadwise shared partners: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of dyads in the network with exactly d[i] shared partners. This term can be used with directed and undirected networks. |
directed, undirected, binary |
dyadcov(x, attrname=NULL) Dyadic covariate: #’ This term adds three statistics to the model, each equal to the sum of the covariate values for all dyads occupying one of the three possible non-empty dyad states (mutual, upper-triangular asymmetric, and lower-triangular asymmetric dyads, respectively), with the empty or null state serving as a reference category. If the network is undirected, x is either a matrix of edgewise covariates, or a network; if the latter, optional argument attrname provides the name of the edge attribute to use for edge values. This term adds one statistic to the model, equal to the sum of the covariate values for each edge appearing in the network. The edgecov and dyadcov terms are equivalent for undirected networks. |
categorical nodal attribute, directed, dyad-independent, undirected, binary |
edgecov(x, attrname=NULL) edgecov(x, attrname=NULL, form=“sum”) Edge covariate: This term adds one statistic to the model, equal to the sum of the covariate values for each edge appearing in the network. The edgecov term applies to both directed and undirected networks. For undirected networks the covariates are also assumed to be undirected. The edgecov and dyadcov terms are equivalent for undirected networks. |
directed, dyad-independent, frequently-used, undirected, binary, valued |
edges edges Edges: This term adds one network statistic equal to the number of edges (i.e. nonzero values) in the network. For undirected networks, edges is equal to kstar(1); for directed networks, edges is equal to both ostar(1) and istar(1). |
directed, dyad-independent, undirected, binary, valued |
nonzero Edges: This term adds one network statistic equal to the number of edges (i.e. nonzero values) in the network. For undirected networks, edges is equal to kstar(1); for directed networks, edges is equal to both ostar(1) and istar(1). |
directed, dyad-independent, undirected, binary, valued |
equalto(value=0, tolerance=0) Number of dyads with values equal to a specific value: Adds one statistic equal to the number of dyads whose values are within tolerance of value , i.e., between value-tolerance and value+tolerance , inclusive. |
directed, dyad-independent, undirected, valued |
esp(d) Edgewise shared partners: This is just like the dsp term, except this term adds one network statistic to the model for each element in d where the i th such statistic equals the number of edges (rather than dyads) in the network with exactly d[i] shared partners. This term can be used with directed and undirected networks. |
directed, undirected, binary |
greaterthan(threshold=0) Number of dyads with values strictly greater than a threshold: Adds the number of statistics equal to the length of threshold equaling to the number of dyads whose values exceed the corresponding element of threshold . |
directed, dyad-independent, undirected, valued |
gwb1degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) |
bipartite, curved, undirected, binary |
gwb1degreeL(decay, fixed=FALSE, cutoff=30, levels=NULL, Ls=NULL) Geometrically weighted degree distribution for the first mode in a bipartite (aka two-mode) network: |
bipartite, curved, undirected, binary |
gwb1dsp(decay=0, fixed=FALSE, cutoff=30) Geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition: This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared partner distribution for dyads in the first bipartition with decay parameter decay parameter, which should be non-negative. This term can only be used with bipartite networks. |
bipartite, curved, undirected, binary |
gwb2degree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) Geometrically weighted degree distribution for the second mode in a bipartite network: This term adds one network statistic to the model equal to the weighted degree distribution with decay controlled by the which should be non-negative, for nodes in the second mode of a bipartite network. The second mode of a bipartite network object is sometimes known as the “event” mode. |
bipartite, curved, undirected, binary |
gwb2degreeL(decay, fixed=FALSE, attrname=NULL, cutoff=30, levels=NULL, Ls=NULL) Geometrically weighted degree distribution for the second mode in a bipartite (aka two-mode) network: |
bipartite, curved, undirected, binary |
gwb2dsp(decay=0, fixed=FALSE, cutoff=30) Geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition: This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared partner distribution for dyads in the second bipartition with decay parameter decay parameter, which should be non-negative. This term can only be used with bipartite networks. |
bipartite, curved, undirected, binary |
gwdegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) Geometrically weighted degree distribution: This term adds one network statistic to the model equal to the weighted degree distribution with decay controlled by the decay parameter, which should be non-negative. |
curved, frequently-used, undirected, binary |
gwdegreeL(decay, fixed=FALSE, attrname=NULL, cutoff=30, levels=NULL) Geometrically weighted degree distribution: |
curved, frequently-used, undirected, binary |
gwdsp(decay, fixed=FALSE, cutoff=30) Geometrically weighted dyadwise shared partner distribution: This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared partner distribution with decay parameter decay parameter, which should be non-negative. This term can be used with directed and undirected networks. |
curved, directed, undirected, binary |
gwesp(decay, fixed=FALSE, cutoff=30) Geometrically weighted edgewise shared partner distribution: This term is just like gwdsp except it adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared partner distribution with decay parameter decay parameter, which should be non-negative. This term can be used with directed and undirected networks. |
curved, directed, frequently-used, undirected, binary |
gwidegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) Geometrically weighted in-degree distribution: This term adds one network statistic to the model equal to the weighted in-degree distribution with decay parameter decay parameter, which should be non-negative. This term can only be used with directed networks. |
curved, directed, binary |
gwidegreeL(decay, fixed=FALSE, attrname=NULL, cutoff=30, levels=NULL, Ls=NULL) Geometrically weighted in-degree distribution: |
curved, directed, binary |
gwnsp(decay, fixed=FALSE, cutoff=30) Geometrically weighted nonedgewise shared partner distribution: This term is just like gwesp and gwdsp except it adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared partner distribution with weight parameter decay parameter, which should be non-negative. This term can be used with directed and undirected networks. |
curved, directed, undirected, binary |
gwodegree(decay, fixed=FALSE, attr=NULL, cutoff=30, levels=NULL) Geometrically weighted out-degree distribution: This term adds one network statistic to the model equal to the weighted out-degree distribution with decay parameter decay parameter, which should be non-negative. This term can only be used with directed networks. |
curved, directed, binary |
gwodegreeL(decay, fixed=FALSE, attrname=NULL, cutoff=30, levels=NULL, Ls=NULL) Geometrically weighted out-degree distribution: |
curved, directed, binary |
hamming(x, cov, attrname=NULL) Hamming distance: This term adds one statistic to the model equal to the weighted or unweighted Hamming distance of the network from the network specified by x . Unweighted Hamming distance is defined as the total number of pairs (i,j) (ordered or unordered, depending on whether the network is directed or undirected) on which the two networks differ. If the optional argument cov is specified, then the weighted Hamming distance is computed instead, where each pair (i,j) contributes a pre-specified weight toward the distance when the two networks differ on that pair. |
directed, dyad-independent, undirected, binary |
idegrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) |
categorical nodal attribute, directed, binary |
idegree(d, by=NULL, homophily=FALSE, levels=NULL) In-degree: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of nodes in the network of in-degree d[i] , i.e. the number of nodes with exactly d[i] in-edges. This term can only be used with directed networks; for undirected networks see degree . |
categorical nodal attribute, directed, frequently-used, binary |
idegree1.5 In-degree to the 3/2 power: This term adds one network statistic to the model equaling the sum over the actors of each actor’s indegree taken to the 3/2 power (or, equivalently, multiplied by its square root). This term is analogous to the term of Snijders et al. (2010), equation (12). This term can only be used with directed networks. |
directed, binary |
idegreeL(d, by=NULL, homophily=FALSE, levels=NULL) In-degree: |
categorical nodal attribute, directed, frequently-used, binary |
ininterval(lower=-Inf, upper=+Inf, open=c(TRUE,TRUE)) Number of dyads whose values are in an interval: Adds one statistic equaling to the number of dyads whose values are between lower and upper . |
directed, dyad-independent, undirected, valued |
intransitive Intransitive triads: This term adds one statistic to the model, equal to the number of triads in the network that are intransitive. The intransitive triads are those of type 111D , 201 , 111U , 021C , or 030C in the categorization of Davis and Leinhardt (1972). For details on the 16 possible triad types, see triad.classify in the sna package. Note the distinction from the ctriple term. |
directed, triad-related, binary |
isolatededges Isolated edges: This term adds one statistic to the model equal to the number of isolated edges in the network, i.e., the number of edges each of whose endpoints has degree 1. This term can only be used with undirected networks. |
bipartite, undirected, binary |
isolates Isolates: This term adds one statistic to the model equal to the number of isolates in the network. For an undirected network, an isolate is defined to be any node with degree zero. For a directed network, an isolate is any node with both in-degree and out-degree equal to zero. |
directed, frequently-used, undirected, binary |
istar(k, attr=NULL, levels=NULL) In-stars: This term adds one network statistic to the model for each element in k . The i th such statistic counts the number of distinct k[i] -instars in the network, where a k -instar is defined to be a node N and a set of k different nodes {O_1, , O_k}{O[1], …, O[k]} such that the ties (O_j{}N)(O_j, N) exist for j=1, , k . If attr is specified then the count is over the number of k -instars where all nodes have the same value of the attribute. This term can only be used for directed networks; for undirected networks see kstar . Note that istar(1) is equal to both ostar(1) and edges . |
categorical nodal attribute, directed, binary |
kstar(k, attr=NULL, levels=NULL) k-Stars: This term adds one network statistic to the model for each element in k . The i th such statistic counts the number of distinct k[i] -stars in the network, where a k -star is defined to be a node N and a set of k different nodes {O_1, , O_k}{O[1], …, O[k]} such that the ties {N, O_i}{N, O[i]} exist for i=1, , k . If this is specified then the count is over the number of k -stars where all nodes have the same value of the attribute. This term can only be used for undirected networks; for directed networks, see istar , ostar , twopath and m2star . Note that kstar(1) is equal to edges . |
categorical nodal attribute, undirected, binary |
localtriangle(x) Triangles within neighborhoods: This term adds one statistic to the model equal to the number of triangles in the network between nodes “close to” each other. For an undirected network, a local triangle is defined to be any set of three edges between nodal pairs {(i,j), (j,k), (k,i)} that are in the same neighborhood. For a directed network, a triangle is defined as any set of three edges (i{}j), (j{}k)(i,j), (j,k) and either (k{}i) or (k{}i) where again all nodes are within the same neighborhood. |
directed, triad-related, undirected, binary |
m2star Mixed 2-stars, a.k.a 2-paths: This term adds one statistic to the model, equal to the number of mixed 2-stars in the network, where a mixed 2-star is a pair of distinct edges (i{}j), (j{}k)(i,j), (j,k) . A mixed 2-star is sometimes called a 2-path because it is a directed path of length 2 from i to k via j . However, in the case of a 2-path the focus is usually on the endpoints i and k , whereas for a mixed 2-star the focus is usually on the midpoint j . This term can only be used with directed networks; for undirected networks see kstar(2) . See also twopath . |
directed, binary |
meandeg Mean vertex degree: This term adds one network statistic to the model equal to the average degree of a node. Note that this term is a constant multiple of both edges and density . |
directed, dyad-independent, undirected, binary |
mm(attrs, levels=NULL, levels2=-1) mm(attrs, levels=NULL, levels2=-1, form=“sum”) Mixing matrix cells and margins: attrs is the rows of the mixing matrix and whose RHS gives that for its columns. A one-sided formula (e.g., ~A ) is symmetrized (e.g., A~A ). A two-sided formula with a dot on one side calculates the margins of the mixing matrix, analogously to nodefactor , with A~. calculating the row/sender/b1 margins and .~A calculating the column/receiver/b2 margins. |
categorical nodal attribute, directed, dyad-independent, frequently-used, undirected, binary, valued |
mutual(same=NULL, by=NULL, diff=FALSE, keep=NULL, levels=NULL) mutual(form=“min”,threshold=0) |
directed, frequently-used, binary, valued |
mutualL(same=NULL, diff=FALSE, by=NULL, keep=NULL, Ls=NULL) Mutuality: This term can only be used with directed networks. |
directed, frequently-used, layer-aware, binary |
nearsimmelian Near simmelian triads: This term adds one statistic to the model equal to the number of near Simmelian triads, as defined by Krackhardt and Handcock (2007). This is a sub-graph of size three which is exactly one tie short of being complete. |
directed, triad-related, binary |
nodecov(attr) nodecov(attr, form=“sum”) Main effect of a covariate: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the sum of attr(i) and attr(j) for all edges (i,j) in the network. For categorical attributes, see nodefactor . Note that for directed networks, nodecov equals nodeicov plus nodeocov . |
directed, dyad-independent, frequently-used, quantitative nodal attribute, undirected, binary, valued |
nodemain nodemain(attr, form=“sum”) Main effect of a covariate: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the sum of attr(i) and attr(j) for all edges (i,j) in the network. For categorical attributes, see nodefactor . Note that for directed networks, nodecov equals nodeicov plus nodeocov . |
directed, dyad-independent, frequently-used, quantitative nodal attribute, undirected, binary, valued |
nodecovar(center, transform) Covariance of undirected dyad values incident on each actor: This term adds one statistic equal to {i,j<k} y{i,j}y_{i,k}/(n-2) . This can be viewed as a valued analog of the star(2) statistic. |
directed, valued |
nodefactor(attr, base=1, levels=-1) nodefactor(attr, base=1, levels=-1, form=“sum”) Factor attribute effect: This term adds multiple network statistics to the model, one for each of (a subset of) the unique values of the attr attribute (or each combination of the attributes given). Each of these statistics gives the number of times a node with that attribute or those attributes appears in an edge in the network. |
categorical nodal attribute, directed, dyad-independent, frequently-used, undirected, binary, valued |
nodeicov(attr) nodeicov(attr, form=“sum”) Main effect of a covariate for in-edges: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the total value of attr(j) for all edges (i,j) in the network. This term may only be used with directed networks. For categorical attributes, see nodeifactor . |
directed, frequently-used, quantitative nodal attribute, binary, valued |
nodeicovar(center, transform) Covariance of in-dyad values incident on each actor: This term adds one statistic equal to {i,j,k} y{j,i}y_{k,i}/(n-2) . This can be viewed as a valued analog of the istar(2) statistic. |
directed, valued |
nodeifactor(attr, base=1, levels=-1) nodeifactor(attr, base=1, levels=-1, form=“sum”) |
categorical nodal attribute, directed, dyad-independent, frequently-used, binary, valued |
nodematch(attr, diff=FALSE, keep=NULL, levels=NULL) nodematch(attr, diff=FALSE, keep=NULL, levels=NULL, form=“sum”) |
categorical nodal attribute, directed, dyad-independent, frequently-used, undirected, binary, valued |
match(attr, diff=FALSE, keep=NULL, levels=NULL, form=“sum”) |
categorical nodal attribute, directed, dyad-independent, frequently-used, undirected, binary, valued |
nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1) nodemix(attr, base=NULL, b1levels=NULL, b2levels=NULL, levels=NULL, levels2=-1, form=“sum”) Nodal attribute mixing: By default, this term adds one network statistic to the model for each possible pairing of attribute values. The statistic equals the number of edges in the network in which the nodes have that pairing of values. (When multiple attributes are specified, a statistic is added for each combination of attribute values for those attributes.) In other words, this term produces one statistic for every entry in the mixing matrix for the attribute(s). By default, the ordering of the attribute values is lexicographic: alphabetical (for nominal categories) or numerical (for ordered categories). |
categorical nodal attribute, directed, dyad-independent, frequently-used, undirected, binary, valued |
nodeocov(attr) nodeocov(attr, form=“sum”) Main effect of a covariate for out-edges: This term adds a single network statistic for each quantitative attribute or matrix column to the model equaling the total value of attr(i) for all edges (i,j) in the network. This term may only be used with directed networks. For categorical attributes, see nodeofactor . |
directed, dyad-independent, quantitative nodal attribute, binary, valued |
nodeocovar(center, transform) Covariance of out-dyad values incident on each actor: This term adds one statistic equal to {i,j,k} y{i,j}y_{i,k}/(n-2) . This can be viewed as a valued analog of the ostar(2) statistic. |
directed, valued |
nodeofactor(attr, base=1, levels=-1) nodeofactor(attr, base=1, levels=-1, form=“sum”) Factor attribute effect for out-edges: This term adds multiple network statistics to the model, one for each of (a subset of) the unique values of the attr attribute (or each combination of the attributes given). Each of these statistics gives the number of times a node with that attribute or those attributes appears as the node of origin of a directed tie. |
categorical nodal attribute, directed, dyad-independent, binary, valued |
nsp(d) Nonedgewise shared partners: This is just like the dsp and esp terms, except this term adds one network statistic to the model for each element in d where the i th such statistic equals the number of non-edges (that is, dyads that do not have an edge) in the network with exactly d[i] shared partners. This term can be used with directed and undirected networks. |
directed, undirected, binary |
odegrange(from, to=+Inf, by=NULL, homophily=FALSE, levels=NULL) |
categorical nodal attribute, directed, binary |
odegree(d, by=NULL, homophily=FALSE, levels=NULL) Out-degree: This term adds one network statistic to the model for each element in d ; the i th such statistic equals the number of nodes in the network of out-degree d[i] , i.e. the number of nodes with exactly d[i] out-edges. This term can only be used with directed networks; for undirected networks see degree . |
categorical nodal attribute, directed, frequently-used, binary |
odegree1.5 Out-degree to the 3/2 power: This term adds one network statistic to the model equaling the sum over the actors of each actor’s outdegree taken to the 3/2 power (or, equivalently, multiplied by its square root). This term is analogous to the term of Snijders et al. (2010), equation (12). This term can only be used with directed networks. |
directed, binary |
odegreeL(d, by=NULL, homophily=FALSE, levels=NULL) |
categorical nodal attribute, directed, frequently-used, binary |
opentriad Open triads: This term adds one statistic to the model equal to the number of 2-stars minus three times the number of triangles in the network. It is currently only implemented for undirected networks. |
triad-related, undirected, binary |
ostar(k, attr=NULL, levels=NULL) k-Outstars: This term adds one network statistic to the model for each element in k . The i th such statistic counts the number of distinct k[i] -outstars in the network, where a k -outstar is defined to be a node N and a set of k different nodes {O_1, , O_k}{O[1], …, O[k]} such that the ties (N{}O_j)(N,O_j) exist for j=1, , k . If attr is specified then the count is the number of k -outstars where all nodes have the same value of the attribute. This term can only be used with directed networks; for undirected networks see kstar . |
categorical nodal attribute, directed, binary |
receiver(base=1, nodes=-1) receiver(base=1, nodes=-1, form=“sum”) Receiver effect: This term adds one network statistic for each node equal to the number of in-ties for that node. This measures the popularity of the node. The term for the first node is omitted by default because of linear dependence that arises if this term is used together with edges , but its coefficient can be computed as the negative of the sum of the coefficients of all the other actors. That is, the average coefficient is zero, following the Holland-Leinhardt parametrization of the \(p_1\) model (Holland and Leinhardt, 1981). This term can only be used with directed networks. For undirected networks, see sociality . |
directed, dyad-independent, binary, valued |
sender(base=1, nodes=-1) sender(base=1, nodes=-1, form=“sum”) |
directed, dyad-independent, binary, valued |
simmelian Simmelian triads: This term adds one statistic to the model equal to the number of Simmelian triads, as defined by Krackhardt and Handcock (2007). This is a complete sub-graph of size three. |
directed, triad-related, binary |
simmelianties Ties in simmelian triads: This term adds one statistic to the model equal to the number of ties in the network that are associated with Simmelian triads, as defined by Krackhardt and Handcock (2007). Each Simmelian has six ties in it but, because Simmelians can overlap in terms of nodes (and associated ties), the total number of ties in these Simmelians is less than six times the number of Simmelians. Hence this is a measure of the clustering of Simmelians (given the number of Simmelians). |
directed, triad-related, binary |
smalldiff(attr, cutoff) Number of ties between actors with similar attribute values: This term adds one statistic, having as its value the number of edges in the network for which the incident actors’ attribute values differ less than cutoff ; that is, number of edges between i to j such that abs(attr[i]-attr[j])<cutoff . |
directed, dyad-independent, quantitative nodal attribute, undirected, binary |
smallerthan(threshold=0) Number of dyads with values strictly smaller than a threshold: Adds the number of statistics equal to the length of threshold equaling to the number of dyads whose values are exceeded by the corresponding element of threshold . |
directed, dyad-independent, undirected, valued |
sociality(attr=NULL, base=1, levels=NULL, nodes=-1) sociality(attr=NULL, base=1, levels=NULL, nodes=-1, form=“sum”) Undirected degree: This term adds one network statistic for each node equal to the number of ties of that node. For directed networks, see sender and receiver . |
categorical nodal attribute, dyad-independent, undirected, binary, valued |
sum(pow=1) Sum of dyad values (optionally taken to a power): This term adds one statistic equal to the sum of dyad values taken to the power pow. |
directed, undirected, valued |
threepath Three-trails: For an undirected network, this term adds one statistic equal to the number of 3-trails, where a 3-trail is defined as a trail of length three that traverses three distinct edges. Note that a 3-trail need not include four distinct nodes; in particular, a triangle counts as three 3-trails. For a directed network, this term adds four statistics (or some subset of these four), one for each of the four distinct types of directed three-paths. If the nodes of the path are written from left to right such that the middle edge points to the right (R), then the four types are RRR, RRL, LRR, and LRL. That is, an RRR 3-trail is of the form ijkli–>j–>k–>l , and RRL 3-trail is of the form ijkli–>j–>k<–l , etc. Like in the undirected case, there is no requirement that the nodes be distinct in a directed 3-trail. However, the three edges must all be distinct. Thus, a mutual tie iji<–>j does not count as a 3-trail of the form ijiji–>j–>i<–j ; however, in the subnetwork ij ki<–>j–>k , there are two directed 3-trails, one LRR ( kjijk<–j–>i–>j ) and one RRR ( jijkk<–j–>i–>j ). |
directed, triad-related, undirected, binary |
threetrail(keep=NULL, levels=NULL) Three-trails: For an undirected network, this term adds one statistic equal to the number of 3-trails, where a 3-trail is defined as a trail of length three that traverses three distinct edges. Note that a 3-trail need not include four distinct nodes; in particular, a triangle counts as three 3-trails. For a directed network, this term adds four statistics (or some subset of these four), one for each of the four distinct types of directed three-paths. If the nodes of the path are written from left to right such that the middle edge points to the right (R), then the four types are RRR, RRL, LRR, and LRL. That is, an RRR 3-trail is of the form ijkli–>j–>k–>l , and RRL 3-trail is of the form ijkli–>j–>k<–l , etc. Like in the undirected case, there is no requirement that the nodes be distinct in a directed 3-trail. However, the three edges must all be distinct. Thus, a mutual tie iji<–>j does not count as a 3-trail of the form ijiji–>j–>i<–j ; however, in the subnetwork ij ki<–>j–>k , there are two directed 3-trails, one LRR ( kjijk<–j–>i–>j ) and one RRR ( jijkk<–j–>i–>j ). |
directed, triad-related, undirected, binary |
transitive Transitive triads: This term adds one statistic to the model, equal to the number of triads in the network that are transitive. The transitive triads are those of type 120D , 030T , 120U , or 300 in the categorization of Davis and Leinhardt (1972). For details on the 16 possible triad types, see ?triad.classify in the sna package. Note the distinction from the ttriple term. This term can only be used with directed networks. |
directed, triad-related, binary |
transitiveties(attr=NULL, levels=NULL) Transitive ties: This term adds one statistic, equal to the number of ties iji–>j such that there exists a two-path from i to j . (Related to the ttriple term.) |
categorical nodal attribute, directed, triad-related, undirected, binary |
transitiveweights(twopath=“min”, combine=“max”, affect=“min”) Transitive weights: This statistic implements the transitive weights statistic defined by Krivitsky (2012), Equation 13. For each of these options, the first (and the default) is more stable but also more conservative, while the second is more sensitive but more likely to induce a multimodal distribution of networks. |
directed, non-negative, triad-related, undirected, valued |
triadcensus(levels) Triad census: For a directed network, this term adds one network statistic for each of an arbitrary subset of the 16 possible types of triads categorized by Davis and Leinhardt (1972) as 003, 012, 102, 021D, 021U, 021C, 111D, 111U, 030T, 030C, 201, 120D, 120U, 120C, 210, and 300 . Note that at least one category should be dropped; otherwise a linear dependency will exist among the 16 statistics, since they must sum to the total number of three-node sets. By default, the category 003 , which is the category of completely empty three-node sets, is dropped. This is considered category zero, and the others are numbered 1 through 15 in the order given above. Each statistic is the count of the corresponding triad type in the network. For details on the 16 types, see ?triad.classify in the sna package, on which this code is based. For an undirected network, the triad census is over the four types defined by the number of ties (i.e., 0, 1, 2, and 3). |
directed, triad-related, undirected, binary |
triangle(attr=NULL, diff=FALSE, levels=NULL) Triangles: By default, this term adds one statistic to the model equal to the number of triangles in the network. For an undirected network, a triangle is defined to be any set {(i,j), (j,k), (k,i)} of three edges. For a directed network, a triangle is defined as any set of three edges (i{}j)(i,j) and (j{}k)(j,k) and either (k{}i)(k,i) or (k{}i)(i,k) . The former case is called a “transitive triple” and the latter is called a “cyclic triple”, so in the case of a directed network, triangle equals ttriple plus ctriple — thus at most two of these three terms can be in a model. |
categorical nodal attribute, directed, frequently-used, triad-related, undirected, binary |
tripercent(attr=NULL, diff=FALSE, levels=NULL) Triangle percentage: By default, this term adds one statistic to the model equal to 100 times the ratio of the number of triangles in the network to the sum of the number of triangles and the number of 2-stars not in triangles (the latter is considered a potential but incomplete triangle). In case the denominator equals zero, the statistic is defined to be zero. For the definition of triangle, see triangle . This is often called the mean correlation coefficient. This term can only be used with undirected networks; for directed networks, it is difficult to define the numerator and denominator in a consistent and meaningful way. |
categorical nodal attribute, triad-related, undirected, binary |
ttriple(attr=NULL, diff=FALSE, levels=NULL) Transitive triples: By default, this term adds one statistic to the model, equal to the number of transitive triples in the network, defined as a set of edges {(i{}j), j{}k), (i{}k)}{(i,j), (j,k), (i,k)} . Note that triangle equals ttriple+ctriple for a directed network, so at most two of the three terms can be in a model. |
categorical nodal attribute, directed, triad-related, binary |
ttriad Transitive triples: By default, this term adds one statistic to the model, equal to the number of transitive triples in the network, defined as a set of edges {(i{}j), j{}k), (i{}k)}{(i,j), (j,k), (i,k)} . Note that triangle equals ttriple+ctriple for a directed network, so at most two of the three terms can be in a model. |
categorical nodal attribute, directed, triad-related, binary |
twopath 2-Paths: This term adds one statistic to the model, equal to the number of 2-paths in the network. For a directed network this is defined as a pair of edges (i{}j), (j{}k)(i,j), (j,k) , where i and j must be distinct. That is, it is a directed path of length 2 from i to k via j . For directed networks a 2-path is also a mixed 2-star but the interpretation is usually different; see m2star . For undirected networks a twopath is defined as a pair of edges {i,j}, {j,k} . That is, it is an undirected path of length 2 from i to k via j , also known as a 2-star. |
directed, undirected, binary |
twostarL(Ls, type, distinct=TRUE) Multilayer two-star: |
directed, layer-aware, undirected, binary |
Note that currently the keywords are somewhat ambiguous in their exclusivity. For example, a term marked as ‘directed’ can not be used with an undirected network, but a term not marked with either ‘directed’ or ‘undirected’ can be used with both. (rename to ‘directed-only’ ?)
$operator \(operator\)link B-ergmTerm Curve-ergmTerm “B-ergmTerm” “Curve-ergmTerm” Exp-ergmTerm F-ergmTerm “Exp-ergmTerm” “F-ergmTerm” L-ergmTerm Label-ergmTerm “L-ergmTerm” “Label-ergmTerm” Log-ergmTerm N-ergmTerm “Log-ergmTerm” “N-ergmTerm” NodematchFilter-ergmTerm Offset-ergmTerm “NodematchFilter-ergmTerm” “Offset-ergmTerm” Prod-ergmTerm S-ergmTerm “Prod-ergmTerm” “S-ergmTerm” Sum-operator-ergmTerm Symmetrize-ergmTerm “Sum-operator-ergmTerm” “Symmetrize-ergmTerm”
\(operator\)name B-ergmTerm Curve-ergmTerm Exp-ergmTerm “B” “Curve” “Exp” F-ergmTerm L-ergmTerm Label-ergmTerm “F” “L” “Label” Log-ergmTerm N-ergmTerm NodematchFilter-ergmTerm “Log” “N” “NodematchFilter” Offset-ergmTerm Prod-ergmTerm S-ergmTerm “Offset” “Prod” “S” Sum-operator-ergmTerm Symmetrize-ergmTerm “Sum” “Symmetrize”
$valued \(valued\)link B-ergmTerm Curve-ergmTerm “B-ergmTerm” “Curve-ergmTerm” Exp-ergmTerm Label-ergmTerm “Exp-ergmTerm” “Label-ergmTerm” Log-ergmTerm N-ergmTerm “Log-ergmTerm” “N-ergmTerm” Prod-ergmTerm Sum-operator-ergmTerm “Prod-ergmTerm” “Sum-operator-ergmTerm” absdiff-ergmTerm absdiffcat-ergmTerm “absdiff-ergmTerm” “absdiffcat-ergmTerm” atleast-ergmTerm atmost-ergmTerm “atleast-ergmTerm” “atmost-ergmTerm” b1cov-ergmTerm b1factor-ergmTerm “b1cov-ergmTerm” “b1factor-ergmTerm” b1sociality-ergmTerm b2cov-ergmTerm “b1sociality-ergmTerm” “b2cov-ergmTerm” b2factor-ergmTerm b2sociality-ergmTerm “b2factor-ergmTerm” “b2sociality-ergmTerm” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties-ergmTerm” “cyclicalweights-ergmTerm” diff-ergmTerm edgecov-ergmTerm “diff-ergmTerm” “edgecov-ergmTerm” edges-ergmTerm equalto-ergmTerm “edges-ergmTerm” “equalto-ergmTerm” greaterthan-ergmTerm ininterval-ergmTerm “greaterthan-ergmTerm” “ininterval-ergmTerm” mm-ergmTerm mutual-ergmTerm “mm-ergmTerm” “mutual-ergmTerm” nodecov-ergmTerm nodecovar-ergmTerm “nodecov-ergmTerm” “nodecovar-ergmTerm” nodefactor-ergmTerm nodeicov-ergmTerm “nodefactor-ergmTerm” “nodeicov-ergmTerm” nodeicovar-ergmTerm nodeifactor-ergmTerm “nodeicovar-ergmTerm” “nodeifactor-ergmTerm” nodematch-ergmTerm nodemix-ergmTerm “nodematch-ergmTerm” “nodemix-ergmTerm” nodeocov-ergmTerm nodeocovar-ergmTerm “nodeocov-ergmTerm” “nodeocovar-ergmTerm” nodeofactor-ergmTerm receiver-ergmTerm “nodeofactor-ergmTerm” “receiver-ergmTerm” sender-ergmTerm smallerthan-ergmTerm “sender-ergmTerm” “smallerthan-ergmTerm” sociality-ergmTerm sum-ergmTerm “sociality-ergmTerm” “sum-ergmTerm” transitiveweights-ergmTerm “transitiveweights-ergmTerm”
\(valued\)name B-ergmTerm Curve-ergmTerm “B” “Curve” Exp-ergmTerm Label-ergmTerm “Exp” “Label” Log-ergmTerm N-ergmTerm “Log” “N” Prod-ergmTerm Sum-operator-ergmTerm “Prod” “Sum” absdiff-ergmTerm absdiffcat-ergmTerm “absdiff” “absdiffcat” atleast-ergmTerm atmost-ergmTerm “atleast” “atmost” b1cov-ergmTerm b1factor-ergmTerm “b1cov” “b1factor” b1sociality-ergmTerm b2cov-ergmTerm “b1sociality” “b2cov” b2factor-ergmTerm b2sociality-ergmTerm “b2factor” “b2sociality” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties” “cyclicalweights” diff-ergmTerm edgecov-ergmTerm “diff” “edgecov” edges-ergmTerm equalto-ergmTerm “edges” “equalto” greaterthan-ergmTerm ininterval-ergmTerm “greaterthan” “ininterval” mm-ergmTerm mutual-ergmTerm “mm” “mutual” nodecov-ergmTerm nodecovar-ergmTerm “nodecov” “nodecovar” nodefactor-ergmTerm nodeicov-ergmTerm “nodefactor” “nodeicov” nodeicovar-ergmTerm nodeifactor-ergmTerm “nodeicovar” “nodeifactor” nodematch-ergmTerm nodemix-ergmTerm “nodematch” “nodemix” nodeocov-ergmTerm nodeocovar-ergmTerm “nodeocov” “nodeocovar” nodeofactor-ergmTerm receiver-ergmTerm “nodeofactor” “receiver” sender-ergmTerm smallerthan-ergmTerm “sender” “smallerthan” sociality-ergmTerm sum-ergmTerm “sociality” “sum” transitiveweights-ergmTerm “transitiveweights”
$directed \(directed\)link CMBL-ergmTerm N-ergmTerm “CMBL-ergmTerm” “N-ergmTerm” Symmetrize-ergmTerm absdiff-ergmTerm “Symmetrize-ergmTerm” “absdiff-ergmTerm” absdiffcat-ergmTerm asymmetric-ergmTerm “absdiffcat-ergmTerm” “asymmetric-ergmTerm” atleast-ergmTerm atmost-ergmTerm “atleast-ergmTerm” “atmost-ergmTerm” attrcov-ergmTerm balance-ergmTerm “attrcov-ergmTerm” “balance-ergmTerm” ctriple-ergmTerm cycle-ergmTerm “ctriple-ergmTerm” “cycle-ergmTerm” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties-ergmTerm” “cyclicalweights-ergmTerm” ddsp-ergmTerm ddspL-ergmTerm “ddsp-ergmTerm” “ddspL-ergmTerm” degreeL-ergmTerm density-ergmTerm “degreeL-ergmTerm” “density-ergmTerm” desp-ergmTerm despL-ergmTerm “desp-ergmTerm” “despL-ergmTerm” dgwdsp-ergmTerm dgwdspL-ergmTerm “dgwdsp-ergmTerm” “dgwdspL-ergmTerm” dgwesp-ergmTerm dgwespL-ergmTerm “dgwesp-ergmTerm” “dgwespL-ergmTerm” dgwnsp-ergmTerm dgwnspL-ergmTerm “dgwnsp-ergmTerm” “dgwnspL-ergmTerm” diff-ergmTerm dnsp-ergmTerm “diff-ergmTerm” “dnsp-ergmTerm” dnspL-ergmTerm dsp-ergmTerm “dnspL-ergmTerm” “dsp-ergmTerm” dyadcov-ergmTerm edgecov-ergmTerm “dyadcov-ergmTerm” “edgecov-ergmTerm” edges-ergmTerm equalto-ergmTerm “edges-ergmTerm” “equalto-ergmTerm” esp-ergmTerm greaterthan-ergmTerm “esp-ergmTerm” “greaterthan-ergmTerm” gwdsp-ergmTerm gwesp-ergmTerm “gwdsp-ergmTerm” “gwesp-ergmTerm” gwidegree-ergmTerm gwidegreeL-ergmTerm “gwidegree-ergmTerm” “gwidegreeL-ergmTerm” gwnsp-ergmTerm gwodegree-ergmTerm “gwnsp-ergmTerm” “gwodegree-ergmTerm” gwodegreeL-ergmTerm hamming-ergmTerm “gwodegreeL-ergmTerm” “hamming-ergmTerm” idegrange-ergmTerm idegree-ergmTerm “idegrange-ergmTerm” “idegree-ergmTerm” idegree1.5-ergmTerm idegreeL-ergmTerm “idegree1.5-ergmTerm” “idegreeL-ergmTerm” ininterval-ergmTerm intransitive-ergmTerm “ininterval-ergmTerm” “intransitive-ergmTerm” isolates-ergmTerm istar-ergmTerm “isolates-ergmTerm” “istar-ergmTerm” localtriangle-ergmTerm m2star-ergmTerm “localtriangle-ergmTerm” “m2star-ergmTerm” meandeg-ergmTerm mm-ergmTerm “meandeg-ergmTerm” “mm-ergmTerm” mutual-ergmTerm mutualL-ergmTerm “mutual-ergmTerm” “mutualL-ergmTerm” nearsimmelian-ergmTerm nodecov-ergmTerm “nearsimmelian-ergmTerm” “nodecov-ergmTerm” nodecovar-ergmTerm nodefactor-ergmTerm “nodecovar-ergmTerm” “nodefactor-ergmTerm” nodeicov-ergmTerm nodeicovar-ergmTerm “nodeicov-ergmTerm” “nodeicovar-ergmTerm” nodeifactor-ergmTerm nodematch-ergmTerm “nodeifactor-ergmTerm” “nodematch-ergmTerm” nodemix-ergmTerm nodeocov-ergmTerm “nodemix-ergmTerm” “nodeocov-ergmTerm” nodeocovar-ergmTerm nodeofactor-ergmTerm “nodeocovar-ergmTerm” “nodeofactor-ergmTerm” nsp-ergmTerm odegrange-ergmTerm “nsp-ergmTerm” “odegrange-ergmTerm” odegree-ergmTerm odegree1.5-ergmTerm “odegree-ergmTerm” “odegree1.5-ergmTerm” odegreeL-ergmTerm ostar-ergmTerm “odegreeL-ergmTerm” “ostar-ergmTerm” receiver-ergmTerm sender-ergmTerm “receiver-ergmTerm” “sender-ergmTerm” simmelian-ergmTerm simmelianties-ergmTerm “simmelian-ergmTerm” “simmelianties-ergmTerm” smalldiff-ergmTerm smallerthan-ergmTerm “smalldiff-ergmTerm” “smallerthan-ergmTerm” sum-ergmTerm threetrail-ergmTerm “sum-ergmTerm” “threetrail-ergmTerm” transitive-ergmTerm transitiveties-ergmTerm “transitive-ergmTerm” “transitiveties-ergmTerm” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights-ergmTerm” “triadcensus-ergmTerm” triangle-ergmTerm ttriple-ergmTerm “triangle-ergmTerm” “ttriple-ergmTerm” twopath-ergmTerm twostarL-ergmTerm “twopath-ergmTerm” “twostarL-ergmTerm”
\(directed\)name CMBL-ergmTerm N-ergmTerm “CMBL” “N” Symmetrize-ergmTerm absdiff-ergmTerm “Symmetrize” “absdiff” absdiffcat-ergmTerm asymmetric-ergmTerm “absdiffcat” “asymmetric” atleast-ergmTerm atmost-ergmTerm “atleast” “atmost” attrcov-ergmTerm balance-ergmTerm “attrcov” “balance” ctriple-ergmTerm cycle-ergmTerm “ctriple” “cycle” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties” “cyclicalweights” ddsp-ergmTerm ddspL-ergmTerm “ddsp” “ddspL” degreeL-ergmTerm density-ergmTerm “degreeL” “density” desp-ergmTerm despL-ergmTerm “desp” “despL” dgwdsp-ergmTerm dgwdspL-ergmTerm “dgwdsp” “dgwdspL” dgwesp-ergmTerm dgwespL-ergmTerm “dgwesp” “dgwespL” dgwnsp-ergmTerm dgwnspL-ergmTerm “dgwnsp” “dgwnspL” diff-ergmTerm dnsp-ergmTerm “diff” “dnsp” dnspL-ergmTerm dsp-ergmTerm “dnspL” “dsp” dyadcov-ergmTerm edgecov-ergmTerm “dyadcov” “edgecov” edges-ergmTerm equalto-ergmTerm “edges” “equalto” esp-ergmTerm greaterthan-ergmTerm “esp” “greaterthan” gwdsp-ergmTerm gwesp-ergmTerm “gwdsp” “gwesp” gwidegree-ergmTerm gwidegreeL-ergmTerm “gwidegree” “gwidegreeL” gwnsp-ergmTerm gwodegree-ergmTerm “gwnsp” “gwodegree” gwodegreeL-ergmTerm hamming-ergmTerm “gwodegreeL” “hamming” idegrange-ergmTerm idegree-ergmTerm “idegrange” “idegree” idegree1.5-ergmTerm idegreeL-ergmTerm “idegree1.5” “idegreeL” ininterval-ergmTerm intransitive-ergmTerm “ininterval” “intransitive” isolates-ergmTerm istar-ergmTerm “isolates” “istar” localtriangle-ergmTerm m2star-ergmTerm “localtriangle” “m2star” meandeg-ergmTerm mm-ergmTerm “meandeg” “mm” mutual-ergmTerm mutualL-ergmTerm “mutual” “mutualL” nearsimmelian-ergmTerm nodecov-ergmTerm “nearsimmelian” “nodecov” nodecovar-ergmTerm nodefactor-ergmTerm “nodecovar” “nodefactor” nodeicov-ergmTerm nodeicovar-ergmTerm “nodeicov” “nodeicovar” nodeifactor-ergmTerm nodematch-ergmTerm “nodeifactor” “nodematch” nodemix-ergmTerm nodeocov-ergmTerm “nodemix” “nodeocov” nodeocovar-ergmTerm nodeofactor-ergmTerm “nodeocovar” “nodeofactor” nsp-ergmTerm odegrange-ergmTerm “nsp” “odegrange” odegree-ergmTerm odegree1.5-ergmTerm “odegree” “odegree1.5” odegreeL-ergmTerm ostar-ergmTerm “odegreeL” “ostar” receiver-ergmTerm sender-ergmTerm “receiver” “sender” simmelian-ergmTerm simmelianties-ergmTerm “simmelian” “simmelianties” smalldiff-ergmTerm smallerthan-ergmTerm “smalldiff” “smallerthan” sum-ergmTerm threetrail-ergmTerm “sum” “threetrail” transitive-ergmTerm transitiveties-ergmTerm “transitive” “transitiveties” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights” “triadcensus” triangle-ergmTerm ttriple-ergmTerm “triangle” “ttriple” twopath-ergmTerm twostarL-ergmTerm “twopath” “twostarL”
$layer-aware
\(`layer-aware`\)link CMBL-ergmTerm L-ergmTerm ddspL-ergmTerm despL-ergmTerm “CMBL-ergmTerm” “L-ergmTerm” “ddspL-ergmTerm” “despL-ergmTerm” dgwdspL-ergmTerm dgwespL-ergmTerm dgwnspL-ergmTerm dnspL-ergmTerm “dgwdspL-ergmTerm” “dgwespL-ergmTerm” “dgwnspL-ergmTerm” “dnspL-ergmTerm” mutualL-ergmTerm twostarL-ergmTerm “mutualL-ergmTerm” “twostarL-ergmTerm”
\(`layer-aware`\)name CMBL-ergmTerm L-ergmTerm ddspL-ergmTerm despL-ergmTerm “CMBL” “L” “ddspL” “despL” dgwdspL-ergmTerm dgwespL-ergmTerm dgwnspL-ergmTerm dnspL-ergmTerm “dgwdspL” “dgwespL” “dgwnspL” “dnspL” mutualL-ergmTerm twostarL-ergmTerm “mutualL” “twostarL”
$undirected \(undirected\)link CMBL-ergmTerm N-ergmTerm “CMBL-ergmTerm” “N-ergmTerm” absdiff-ergmTerm absdiffcat-ergmTerm “absdiff-ergmTerm” “absdiffcat-ergmTerm” altkstar-ergmTerm atleast-ergmTerm “altkstar-ergmTerm” “atleast-ergmTerm” atmost-ergmTerm attrcov-ergmTerm “atmost-ergmTerm” “attrcov-ergmTerm” b1concurrent-ergmTerm b1cov-ergmTerm “b1concurrent-ergmTerm” “b1cov-ergmTerm” b1degrange-ergmTerm b1degree-ergmTerm “b1degrange-ergmTerm” “b1degree-ergmTerm” b1degreeL-ergmTerm b1dsp-ergmTerm “b1degreeL-ergmTerm” “b1dsp-ergmTerm” b1factor-ergmTerm b1mindegree-ergmTerm “b1factor-ergmTerm” “b1mindegree-ergmTerm” b1nodematch-ergmTerm b1sociality-ergmTerm “b1nodematch-ergmTerm” “b1sociality-ergmTerm” b1star-ergmTerm b1starmix-ergmTerm “b1star-ergmTerm” “b1starmix-ergmTerm” b1twostar-ergmTerm b2concurrent-ergmTerm “b1twostar-ergmTerm” “b2concurrent-ergmTerm” b2cov-ergmTerm b2degrange-ergmTerm “b2cov-ergmTerm” “b2degrange-ergmTerm” b2degree-ergmTerm b2dsp-ergmTerm “b2degree-ergmTerm” “b2dsp-ergmTerm” b2factor-ergmTerm b2mindegree-ergmTerm “b2factor-ergmTerm” “b2mindegree-ergmTerm” b2nodematch-ergmTerm b2sociality-ergmTerm “b2nodematch-ergmTerm” “b2sociality-ergmTerm” b2star-ergmTerm b2starmix-ergmTerm “b2star-ergmTerm” “b2starmix-ergmTerm” b2twostar-ergmTerm balance-ergmTerm “b2twostar-ergmTerm” “balance-ergmTerm” coincidence-ergmTerm concurrent-ergmTerm “coincidence-ergmTerm” “concurrent-ergmTerm” concurrentties-ergmTerm cycle-ergmTerm “concurrentties-ergmTerm” “cycle-ergmTerm” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties-ergmTerm” “cyclicalweights-ergmTerm” ddspL-ergmTerm degcor-ergmTerm “ddspL-ergmTerm” “degcor-ergmTerm” degcrossprod-ergmTerm degrange-ergmTerm “degcrossprod-ergmTerm” “degrange-ergmTerm” degree-ergmTerm degree1.5-ergmTerm “degree-ergmTerm” “degree1.5-ergmTerm” degreeL-ergmTerm density-ergmTerm “degreeL-ergmTerm” “density-ergmTerm” despL-ergmTerm dgwdspL-ergmTerm “despL-ergmTerm” “dgwdspL-ergmTerm” dgwespL-ergmTerm dgwnspL-ergmTerm “dgwespL-ergmTerm” “dgwnspL-ergmTerm” diff-ergmTerm dnspL-ergmTerm “diff-ergmTerm” “dnspL-ergmTerm” dsp-ergmTerm dyadcov-ergmTerm “dsp-ergmTerm” “dyadcov-ergmTerm” edgecov-ergmTerm edges-ergmTerm “edgecov-ergmTerm” “edges-ergmTerm” equalto-ergmTerm esp-ergmTerm “equalto-ergmTerm” “esp-ergmTerm” greaterthan-ergmTerm gwb1degree-ergmTerm “greaterthan-ergmTerm” “gwb1degree-ergmTerm” gwb1degreeL-ergmTerm gwb1dsp-ergmTerm “gwb1degreeL-ergmTerm” “gwb1dsp-ergmTerm” gwb2degree-ergmTerm gwb2degreeL-ergmTerm “gwb2degree-ergmTerm” “gwb2degreeL-ergmTerm” gwb2dsp-ergmTerm gwdegree-ergmTerm “gwb2dsp-ergmTerm” “gwdegree-ergmTerm” gwdegreeL-ergmTerm gwdsp-ergmTerm “gwdegreeL-ergmTerm” “gwdsp-ergmTerm” gwesp-ergmTerm gwnsp-ergmTerm “gwesp-ergmTerm” “gwnsp-ergmTerm” hamming-ergmTerm ininterval-ergmTerm “hamming-ergmTerm” “ininterval-ergmTerm” isolatededges-ergmTerm isolates-ergmTerm “isolatededges-ergmTerm” “isolates-ergmTerm” kstar-ergmTerm localtriangle-ergmTerm “kstar-ergmTerm” “localtriangle-ergmTerm” meandeg-ergmTerm mm-ergmTerm “meandeg-ergmTerm” “mm-ergmTerm” nodecov-ergmTerm nodefactor-ergmTerm “nodecov-ergmTerm” “nodefactor-ergmTerm” nodematch-ergmTerm nodemix-ergmTerm “nodematch-ergmTerm” “nodemix-ergmTerm” nsp-ergmTerm opentriad-ergmTerm “nsp-ergmTerm” “opentriad-ergmTerm” smalldiff-ergmTerm smallerthan-ergmTerm “smalldiff-ergmTerm” “smallerthan-ergmTerm” sociality-ergmTerm sum-ergmTerm “sociality-ergmTerm” “sum-ergmTerm” threetrail-ergmTerm transitiveties-ergmTerm “threetrail-ergmTerm” “transitiveties-ergmTerm” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights-ergmTerm” “triadcensus-ergmTerm” triangle-ergmTerm tripercent-ergmTerm “triangle-ergmTerm” “tripercent-ergmTerm” twopath-ergmTerm twostarL-ergmTerm “twopath-ergmTerm” “twostarL-ergmTerm”
\(undirected\)name CMBL-ergmTerm N-ergmTerm “CMBL” “N” absdiff-ergmTerm absdiffcat-ergmTerm “absdiff” “absdiffcat” altkstar-ergmTerm atleast-ergmTerm “altkstar” “atleast” atmost-ergmTerm attrcov-ergmTerm “atmost” “attrcov” b1concurrent-ergmTerm b1cov-ergmTerm “b1concurrent” “b1cov” b1degrange-ergmTerm b1degree-ergmTerm “b1degrange” “b1degree” b1degreeL-ergmTerm b1dsp-ergmTerm “b1degreeL” “b1dsp” b1factor-ergmTerm b1mindegree-ergmTerm “b1factor” “b1mindegree” b1nodematch-ergmTerm b1sociality-ergmTerm “b1nodematch” “b1sociality” b1star-ergmTerm b1starmix-ergmTerm “b1star” “b1starmix” b1twostar-ergmTerm b2concurrent-ergmTerm “b1twostar” “b2concurrent” b2cov-ergmTerm b2degrange-ergmTerm “b2cov” “b2degrange” b2degree-ergmTerm b2dsp-ergmTerm “b2degree” “b2dsp” b2factor-ergmTerm b2mindegree-ergmTerm “b2factor” “b2mindegree” b2nodematch-ergmTerm b2sociality-ergmTerm “b2nodematch” “b2sociality” b2star-ergmTerm b2starmix-ergmTerm “b2star” “b2starmix” b2twostar-ergmTerm balance-ergmTerm “b2twostar” “balance” coincidence-ergmTerm concurrent-ergmTerm “coincidence” “concurrent” concurrentties-ergmTerm cycle-ergmTerm “concurrentties” “cycle” cyclicalties-ergmTerm cyclicalweights-ergmTerm “cyclicalties” “cyclicalweights” ddspL-ergmTerm degcor-ergmTerm “ddspL” “degcor” degcrossprod-ergmTerm degrange-ergmTerm “degcrossprod” “degrange” degree-ergmTerm degree1.5-ergmTerm “degree” “degree1.5” degreeL-ergmTerm density-ergmTerm “degreeL” “density” despL-ergmTerm dgwdspL-ergmTerm “despL” “dgwdspL” dgwespL-ergmTerm dgwnspL-ergmTerm “dgwespL” “dgwnspL” diff-ergmTerm dnspL-ergmTerm “diff” “dnspL” dsp-ergmTerm dyadcov-ergmTerm “dsp” “dyadcov” edgecov-ergmTerm edges-ergmTerm “edgecov” “edges” equalto-ergmTerm esp-ergmTerm “equalto” “esp” greaterthan-ergmTerm gwb1degree-ergmTerm “greaterthan” “gwb1degree” gwb1degreeL-ergmTerm gwb1dsp-ergmTerm “gwb1degreeL” “gwb1dsp” gwb2degree-ergmTerm gwb2degreeL-ergmTerm “gwb2degree” “gwb2degreeL” gwb2dsp-ergmTerm gwdegree-ergmTerm “gwb2dsp” “gwdegree” gwdegreeL-ergmTerm gwdsp-ergmTerm “gwdegreeL” “gwdsp” gwesp-ergmTerm gwnsp-ergmTerm “gwesp” “gwnsp” hamming-ergmTerm ininterval-ergmTerm “hamming” “ininterval” isolatededges-ergmTerm isolates-ergmTerm “isolatededges” “isolates” kstar-ergmTerm localtriangle-ergmTerm “kstar” “localtriangle” meandeg-ergmTerm mm-ergmTerm “meandeg” “mm” nodecov-ergmTerm nodefactor-ergmTerm “nodecov” “nodefactor” nodematch-ergmTerm nodemix-ergmTerm “nodematch” “nodemix” nsp-ergmTerm opentriad-ergmTerm “nsp” “opentriad” smalldiff-ergmTerm smallerthan-ergmTerm “smalldiff” “smallerthan” sociality-ergmTerm sum-ergmTerm “sociality” “sum” threetrail-ergmTerm transitiveties-ergmTerm “threetrail” “transitiveties” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights” “triadcensus” triangle-ergmTerm tripercent-ergmTerm “triangle” “tripercent” twopath-ergmTerm twostarL-ergmTerm “twopath” “twostarL”
$binary \(binary\)link CMBL-ergmTerm Curve-ergmTerm “CMBL-ergmTerm” “Curve-ergmTerm” Exp-ergmTerm F-ergmTerm “Exp-ergmTerm” “F-ergmTerm” L-ergmTerm Label-ergmTerm “L-ergmTerm” “Label-ergmTerm” Log-ergmTerm N-ergmTerm “Log-ergmTerm” “N-ergmTerm” NodematchFilter-ergmTerm Offset-ergmTerm “NodematchFilter-ergmTerm” “Offset-ergmTerm” Prod-ergmTerm S-ergmTerm “Prod-ergmTerm” “S-ergmTerm” Sum-operator-ergmTerm Symmetrize-ergmTerm “Sum-operator-ergmTerm” “Symmetrize-ergmTerm” absdiff-ergmTerm absdiffcat-ergmTerm “absdiff-ergmTerm” “absdiffcat-ergmTerm” altkstar-ergmTerm asymmetric-ergmTerm “altkstar-ergmTerm” “asymmetric-ergmTerm” attrcov-ergmTerm b1concurrent-ergmTerm “attrcov-ergmTerm” “b1concurrent-ergmTerm” b1cov-ergmTerm b1degrange-ergmTerm “b1cov-ergmTerm” “b1degrange-ergmTerm” b1degree-ergmTerm b1degreeL-ergmTerm “b1degree-ergmTerm” “b1degreeL-ergmTerm” b1dsp-ergmTerm b1factor-ergmTerm “b1dsp-ergmTerm” “b1factor-ergmTerm” b1mindegree-ergmTerm b1nodematch-ergmTerm “b1mindegree-ergmTerm” “b1nodematch-ergmTerm” b1sociality-ergmTerm b1star-ergmTerm “b1sociality-ergmTerm” “b1star-ergmTerm” b1starmix-ergmTerm b1twostar-ergmTerm “b1starmix-ergmTerm” “b1twostar-ergmTerm” b2concurrent-ergmTerm b2cov-ergmTerm “b2concurrent-ergmTerm” “b2cov-ergmTerm” b2degrange-ergmTerm b2degree-ergmTerm “b2degrange-ergmTerm” “b2degree-ergmTerm” b2dsp-ergmTerm b2factor-ergmTerm “b2dsp-ergmTerm” “b2factor-ergmTerm” b2mindegree-ergmTerm b2nodematch-ergmTerm “b2mindegree-ergmTerm” “b2nodematch-ergmTerm” b2sociality-ergmTerm b2star-ergmTerm “b2sociality-ergmTerm” “b2star-ergmTerm” b2starmix-ergmTerm b2twostar-ergmTerm “b2starmix-ergmTerm” “b2twostar-ergmTerm” balance-ergmTerm coincidence-ergmTerm “balance-ergmTerm” “coincidence-ergmTerm” concurrent-ergmTerm concurrentties-ergmTerm “concurrent-ergmTerm” “concurrentties-ergmTerm” ctriple-ergmTerm cycle-ergmTerm “ctriple-ergmTerm” “cycle-ergmTerm” cyclicalties-ergmTerm ddsp-ergmTerm “cyclicalties-ergmTerm” “ddsp-ergmTerm” ddspL-ergmTerm degcor-ergmTerm “ddspL-ergmTerm” “degcor-ergmTerm” degcrossprod-ergmTerm degrange-ergmTerm “degcrossprod-ergmTerm” “degrange-ergmTerm” degree-ergmTerm degree1.5-ergmTerm “degree-ergmTerm” “degree1.5-ergmTerm” degreeL-ergmTerm density-ergmTerm “degreeL-ergmTerm” “density-ergmTerm” desp-ergmTerm despL-ergmTerm “desp-ergmTerm” “despL-ergmTerm” dgwdsp-ergmTerm dgwdspL-ergmTerm “dgwdsp-ergmTerm” “dgwdspL-ergmTerm” dgwesp-ergmTerm dgwespL-ergmTerm “dgwesp-ergmTerm” “dgwespL-ergmTerm” dgwnsp-ergmTerm dgwnspL-ergmTerm “dgwnsp-ergmTerm” “dgwnspL-ergmTerm” diff-ergmTerm dnsp-ergmTerm “diff-ergmTerm” “dnsp-ergmTerm” dnspL-ergmTerm dsp-ergmTerm “dnspL-ergmTerm” “dsp-ergmTerm” dyadcov-ergmTerm edgecov-ergmTerm “dyadcov-ergmTerm” “edgecov-ergmTerm” edges-ergmTerm esp-ergmTerm “edges-ergmTerm” “esp-ergmTerm” gwb1degree-ergmTerm gwb1degreeL-ergmTerm “gwb1degree-ergmTerm” “gwb1degreeL-ergmTerm” gwb1dsp-ergmTerm gwb2degree-ergmTerm “gwb1dsp-ergmTerm” “gwb2degree-ergmTerm” gwb2degreeL-ergmTerm gwb2dsp-ergmTerm “gwb2degreeL-ergmTerm” “gwb2dsp-ergmTerm” gwdegree-ergmTerm gwdegreeL-ergmTerm “gwdegree-ergmTerm” “gwdegreeL-ergmTerm” gwdsp-ergmTerm gwesp-ergmTerm “gwdsp-ergmTerm” “gwesp-ergmTerm” gwidegree-ergmTerm gwidegreeL-ergmTerm “gwidegree-ergmTerm” “gwidegreeL-ergmTerm” gwnsp-ergmTerm gwodegree-ergmTerm “gwnsp-ergmTerm” “gwodegree-ergmTerm” gwodegreeL-ergmTerm hamming-ergmTerm “gwodegreeL-ergmTerm” “hamming-ergmTerm” idegrange-ergmTerm idegree-ergmTerm “idegrange-ergmTerm” “idegree-ergmTerm” idegree1.5-ergmTerm idegreeL-ergmTerm “idegree1.5-ergmTerm” “idegreeL-ergmTerm” intransitive-ergmTerm isolatededges-ergmTerm “intransitive-ergmTerm” “isolatededges-ergmTerm” isolates-ergmTerm istar-ergmTerm “isolates-ergmTerm” “istar-ergmTerm” kstar-ergmTerm localtriangle-ergmTerm “kstar-ergmTerm” “localtriangle-ergmTerm” m2star-ergmTerm meandeg-ergmTerm “m2star-ergmTerm” “meandeg-ergmTerm” mm-ergmTerm mutual-ergmTerm “mm-ergmTerm” “mutual-ergmTerm” mutualL-ergmTerm nearsimmelian-ergmTerm “mutualL-ergmTerm” “nearsimmelian-ergmTerm” nodecov-ergmTerm nodefactor-ergmTerm “nodecov-ergmTerm” “nodefactor-ergmTerm” nodeicov-ergmTerm nodeifactor-ergmTerm “nodeicov-ergmTerm” “nodeifactor-ergmTerm” nodematch-ergmTerm nodemix-ergmTerm “nodematch-ergmTerm” “nodemix-ergmTerm” nodeocov-ergmTerm nodeofactor-ergmTerm “nodeocov-ergmTerm” “nodeofactor-ergmTerm” nsp-ergmTerm odegrange-ergmTerm “nsp-ergmTerm” “odegrange-ergmTerm” odegree-ergmTerm odegree1.5-ergmTerm “odegree-ergmTerm” “odegree1.5-ergmTerm” odegreeL-ergmTerm opentriad-ergmTerm “odegreeL-ergmTerm” “opentriad-ergmTerm” ostar-ergmTerm receiver-ergmTerm “ostar-ergmTerm” “receiver-ergmTerm” sender-ergmTerm simmelian-ergmTerm “sender-ergmTerm” “simmelian-ergmTerm” simmelianties-ergmTerm smalldiff-ergmTerm “simmelianties-ergmTerm” “smalldiff-ergmTerm” sociality-ergmTerm threetrail-ergmTerm “sociality-ergmTerm” “threetrail-ergmTerm” transitive-ergmTerm transitiveties-ergmTerm “transitive-ergmTerm” “transitiveties-ergmTerm” triadcensus-ergmTerm triangle-ergmTerm “triadcensus-ergmTerm” “triangle-ergmTerm” tripercent-ergmTerm ttriple-ergmTerm “tripercent-ergmTerm” “ttriple-ergmTerm” twopath-ergmTerm twostarL-ergmTerm “twopath-ergmTerm” “twostarL-ergmTerm”
\(binary\)name CMBL-ergmTerm Curve-ergmTerm Exp-ergmTerm “CMBL” “Curve” “Exp” F-ergmTerm L-ergmTerm Label-ergmTerm “F” “L” “Label” Log-ergmTerm N-ergmTerm NodematchFilter-ergmTerm “Log” “N” “NodematchFilter” Offset-ergmTerm Prod-ergmTerm S-ergmTerm “Offset” “Prod” “S” Sum-operator-ergmTerm Symmetrize-ergmTerm absdiff-ergmTerm “Sum” “Symmetrize” “absdiff” absdiffcat-ergmTerm altkstar-ergmTerm asymmetric-ergmTerm “absdiffcat” “altkstar” “asymmetric” attrcov-ergmTerm b1concurrent-ergmTerm b1cov-ergmTerm “attrcov” “b1concurrent” “b1cov” b1degrange-ergmTerm b1degree-ergmTerm b1degreeL-ergmTerm “b1degrange” “b1degree” “b1degreeL” b1dsp-ergmTerm b1factor-ergmTerm b1mindegree-ergmTerm “b1dsp” “b1factor” “b1mindegree” b1nodematch-ergmTerm b1sociality-ergmTerm b1star-ergmTerm “b1nodematch” “b1sociality” “b1star” b1starmix-ergmTerm b1twostar-ergmTerm b2concurrent-ergmTerm “b1starmix” “b1twostar” “b2concurrent” b2cov-ergmTerm b2degrange-ergmTerm b2degree-ergmTerm “b2cov” “b2degrange” “b2degree” b2dsp-ergmTerm b2factor-ergmTerm b2mindegree-ergmTerm “b2dsp” “b2factor” “b2mindegree” b2nodematch-ergmTerm b2sociality-ergmTerm b2star-ergmTerm “b2nodematch” “b2sociality” “b2star” b2starmix-ergmTerm b2twostar-ergmTerm balance-ergmTerm “b2starmix” “b2twostar” “balance” coincidence-ergmTerm concurrent-ergmTerm concurrentties-ergmTerm “coincidence” “concurrent” “concurrentties” ctriple-ergmTerm cycle-ergmTerm cyclicalties-ergmTerm “ctriple” “cycle” “cyclicalties” ddsp-ergmTerm ddspL-ergmTerm degcor-ergmTerm “ddsp” “ddspL” “degcor” degcrossprod-ergmTerm degrange-ergmTerm degree-ergmTerm “degcrossprod” “degrange” “degree” degree1.5-ergmTerm degreeL-ergmTerm density-ergmTerm “degree1.5” “degreeL” “density” desp-ergmTerm despL-ergmTerm dgwdsp-ergmTerm “desp” “despL” “dgwdsp” dgwdspL-ergmTerm dgwesp-ergmTerm dgwespL-ergmTerm “dgwdspL” “dgwesp” “dgwespL” dgwnsp-ergmTerm dgwnspL-ergmTerm diff-ergmTerm “dgwnsp” “dgwnspL” “diff” dnsp-ergmTerm dnspL-ergmTerm dsp-ergmTerm “dnsp” “dnspL” “dsp” dyadcov-ergmTerm edgecov-ergmTerm edges-ergmTerm “dyadcov” “edgecov” “edges” esp-ergmTerm gwb1degree-ergmTerm gwb1degreeL-ergmTerm “esp” “gwb1degree” “gwb1degreeL” gwb1dsp-ergmTerm gwb2degree-ergmTerm gwb2degreeL-ergmTerm “gwb1dsp” “gwb2degree” “gwb2degreeL” gwb2dsp-ergmTerm gwdegree-ergmTerm gwdegreeL-ergmTerm “gwb2dsp” “gwdegree” “gwdegreeL” gwdsp-ergmTerm gwesp-ergmTerm gwidegree-ergmTerm “gwdsp” “gwesp” “gwidegree” gwidegreeL-ergmTerm gwnsp-ergmTerm gwodegree-ergmTerm “gwidegreeL” “gwnsp” “gwodegree” gwodegreeL-ergmTerm hamming-ergmTerm idegrange-ergmTerm “gwodegreeL” “hamming” “idegrange” idegree-ergmTerm idegree1.5-ergmTerm idegreeL-ergmTerm “idegree” “idegree1.5” “idegreeL” intransitive-ergmTerm isolatededges-ergmTerm isolates-ergmTerm “intransitive” “isolatededges” “isolates” istar-ergmTerm kstar-ergmTerm localtriangle-ergmTerm “istar” “kstar” “localtriangle” m2star-ergmTerm meandeg-ergmTerm mm-ergmTerm “m2star” “meandeg” “mm” mutual-ergmTerm mutualL-ergmTerm nearsimmelian-ergmTerm “mutual” “mutualL” “nearsimmelian” nodecov-ergmTerm nodefactor-ergmTerm nodeicov-ergmTerm “nodecov” “nodefactor” “nodeicov” nodeifactor-ergmTerm nodematch-ergmTerm nodemix-ergmTerm “nodeifactor” “nodematch” “nodemix” nodeocov-ergmTerm nodeofactor-ergmTerm nsp-ergmTerm “nodeocov” “nodeofactor” “nsp” odegrange-ergmTerm odegree-ergmTerm odegree1.5-ergmTerm “odegrange” “odegree” “odegree1.5” odegreeL-ergmTerm opentriad-ergmTerm ostar-ergmTerm “odegreeL” “opentriad” “ostar” receiver-ergmTerm sender-ergmTerm simmelian-ergmTerm “receiver” “sender” “simmelian” simmelianties-ergmTerm smalldiff-ergmTerm sociality-ergmTerm “simmelianties” “smalldiff” “sociality” threetrail-ergmTerm transitive-ergmTerm transitiveties-ergmTerm “threetrail” “transitive” “transitiveties” triadcensus-ergmTerm triangle-ergmTerm tripercent-ergmTerm “triadcensus” “triangle” “tripercent” ttriple-ergmTerm twopath-ergmTerm twostarL-ergmTerm “ttriple” “twopath” “twostarL”
$dyad-independent
\(`dyad-independent`\)link absdiff-ergmTerm absdiffcat-ergmTerm asymmetric-ergmTerm “absdiff-ergmTerm” “absdiffcat-ergmTerm” “asymmetric-ergmTerm” atleast-ergmTerm atmost-ergmTerm attrcov-ergmTerm “atleast-ergmTerm” “atmost-ergmTerm” “attrcov-ergmTerm” b1cov-ergmTerm b1factor-ergmTerm b1nodematch-ergmTerm “b1cov-ergmTerm” “b1factor-ergmTerm” “b1nodematch-ergmTerm” b1sociality-ergmTerm b2cov-ergmTerm b2factor-ergmTerm “b1sociality-ergmTerm” “b2cov-ergmTerm” “b2factor-ergmTerm” b2nodematch-ergmTerm b2sociality-ergmTerm density-ergmTerm “b2nodematch-ergmTerm” “b2sociality-ergmTerm” “density-ergmTerm” diff-ergmTerm dyadcov-ergmTerm edgecov-ergmTerm “diff-ergmTerm” “dyadcov-ergmTerm” “edgecov-ergmTerm” edges-ergmTerm equalto-ergmTerm greaterthan-ergmTerm “edges-ergmTerm” “equalto-ergmTerm” “greaterthan-ergmTerm” hamming-ergmTerm ininterval-ergmTerm meandeg-ergmTerm “hamming-ergmTerm” “ininterval-ergmTerm” “meandeg-ergmTerm” mm-ergmTerm nodecov-ergmTerm nodefactor-ergmTerm “mm-ergmTerm” “nodecov-ergmTerm” “nodefactor-ergmTerm” nodeifactor-ergmTerm nodematch-ergmTerm nodemix-ergmTerm “nodeifactor-ergmTerm” “nodematch-ergmTerm” “nodemix-ergmTerm” nodeocov-ergmTerm nodeofactor-ergmTerm receiver-ergmTerm “nodeocov-ergmTerm” “nodeofactor-ergmTerm” “receiver-ergmTerm” sender-ergmTerm smalldiff-ergmTerm smallerthan-ergmTerm “sender-ergmTerm” “smalldiff-ergmTerm” “smallerthan-ergmTerm” sociality-ergmTerm “sociality-ergmTerm”
\(`dyad-independent`\)name absdiff-ergmTerm absdiffcat-ergmTerm asymmetric-ergmTerm “absdiff” “absdiffcat” “asymmetric” atleast-ergmTerm atmost-ergmTerm attrcov-ergmTerm “atleast” “atmost” “attrcov” b1cov-ergmTerm b1factor-ergmTerm b1nodematch-ergmTerm “b1cov” “b1factor” “b1nodematch” b1sociality-ergmTerm b2cov-ergmTerm b2factor-ergmTerm “b1sociality” “b2cov” “b2factor” b2nodematch-ergmTerm b2sociality-ergmTerm density-ergmTerm “b2nodematch” “b2sociality” “density” diff-ergmTerm dyadcov-ergmTerm edgecov-ergmTerm “diff” “dyadcov” “edgecov” edges-ergmTerm equalto-ergmTerm greaterthan-ergmTerm “edges” “equalto” “greaterthan” hamming-ergmTerm ininterval-ergmTerm meandeg-ergmTerm “hamming” “ininterval” “meandeg” mm-ergmTerm nodecov-ergmTerm nodefactor-ergmTerm “mm” “nodecov” “nodefactor” nodeifactor-ergmTerm nodematch-ergmTerm nodemix-ergmTerm “nodeifactor” “nodematch” “nodemix” nodeocov-ergmTerm nodeofactor-ergmTerm receiver-ergmTerm “nodeocov” “nodeofactor” “receiver” sender-ergmTerm smalldiff-ergmTerm smallerthan-ergmTerm “sender” “smalldiff” “smallerthan” sociality-ergmTerm “sociality”
$quantitative nodal attribute
\(`quantitative nodal attribute`\)link absdiff-ergmTerm b1cov-ergmTerm b2cov-ergmTerm “absdiff-ergmTerm” “b1cov-ergmTerm” “b2cov-ergmTerm” diff-ergmTerm nodecov-ergmTerm nodeicov-ergmTerm “diff-ergmTerm” “nodecov-ergmTerm” “nodeicov-ergmTerm” nodeocov-ergmTerm smalldiff-ergmTerm “nodeocov-ergmTerm” “smalldiff-ergmTerm”
\(`quantitative nodal attribute`\)name absdiff-ergmTerm b1cov-ergmTerm b2cov-ergmTerm diff-ergmTerm “absdiff” “b1cov” “b2cov” “diff” nodecov-ergmTerm nodeicov-ergmTerm nodeocov-ergmTerm smalldiff-ergmTerm “nodecov” “nodeicov” “nodeocov” “smalldiff”
$categorical nodal attribute
\(`categorical nodal attribute`\)link absdiffcat-ergmTerm altkstar-ergmTerm b1concurrent-ergmTerm “absdiffcat-ergmTerm” “altkstar-ergmTerm” “b1concurrent-ergmTerm” b1degree-ergmTerm b1degreeL-ergmTerm b1factor-ergmTerm “b1degree-ergmTerm” “b1degreeL-ergmTerm” “b1factor-ergmTerm” b1nodematch-ergmTerm b1star-ergmTerm b1starmix-ergmTerm “b1nodematch-ergmTerm” “b1star-ergmTerm” “b1starmix-ergmTerm” b1twostar-ergmTerm b2degree-ergmTerm b2factor-ergmTerm “b1twostar-ergmTerm” “b2degree-ergmTerm” “b2factor-ergmTerm” b2nodematch-ergmTerm b2star-ergmTerm b2starmix-ergmTerm “b2nodematch-ergmTerm” “b2star-ergmTerm” “b2starmix-ergmTerm” b2twostar-ergmTerm concurrent-ergmTerm concurrentties-ergmTerm “b2twostar-ergmTerm” “concurrent-ergmTerm” “concurrentties-ergmTerm” ctriple-ergmTerm degrange-ergmTerm degree-ergmTerm “ctriple-ergmTerm” “degrange-ergmTerm” “degree-ergmTerm” degreeL-ergmTerm dyadcov-ergmTerm idegrange-ergmTerm “degreeL-ergmTerm” “dyadcov-ergmTerm” “idegrange-ergmTerm” idegree-ergmTerm idegreeL-ergmTerm istar-ergmTerm “idegree-ergmTerm” “idegreeL-ergmTerm” “istar-ergmTerm” kstar-ergmTerm mm-ergmTerm nodefactor-ergmTerm “kstar-ergmTerm” “mm-ergmTerm” “nodefactor-ergmTerm” nodeifactor-ergmTerm nodematch-ergmTerm nodemix-ergmTerm “nodeifactor-ergmTerm” “nodematch-ergmTerm” “nodemix-ergmTerm” nodeofactor-ergmTerm odegrange-ergmTerm odegree-ergmTerm “nodeofactor-ergmTerm” “odegrange-ergmTerm” “odegree-ergmTerm” odegreeL-ergmTerm ostar-ergmTerm sociality-ergmTerm “odegreeL-ergmTerm” “ostar-ergmTerm” “sociality-ergmTerm” transitiveties-ergmTerm triangle-ergmTerm tripercent-ergmTerm “transitiveties-ergmTerm” “triangle-ergmTerm” “tripercent-ergmTerm” ttriple-ergmTerm “ttriple-ergmTerm”
\(`categorical nodal attribute`\)name absdiffcat-ergmTerm altkstar-ergmTerm b1concurrent-ergmTerm “absdiffcat” “altkstar” “b1concurrent” b1degree-ergmTerm b1degreeL-ergmTerm b1factor-ergmTerm “b1degree” “b1degreeL” “b1factor” b1nodematch-ergmTerm b1star-ergmTerm b1starmix-ergmTerm “b1nodematch” “b1star” “b1starmix” b1twostar-ergmTerm b2degree-ergmTerm b2factor-ergmTerm “b1twostar” “b2degree” “b2factor” b2nodematch-ergmTerm b2star-ergmTerm b2starmix-ergmTerm “b2nodematch” “b2star” “b2starmix” b2twostar-ergmTerm concurrent-ergmTerm concurrentties-ergmTerm “b2twostar” “concurrent” “concurrentties” ctriple-ergmTerm degrange-ergmTerm degree-ergmTerm “ctriple” “degrange” “degree” degreeL-ergmTerm dyadcov-ergmTerm idegrange-ergmTerm “degreeL” “dyadcov” “idegrange” idegree-ergmTerm idegreeL-ergmTerm istar-ergmTerm “idegree” “idegreeL” “istar” kstar-ergmTerm mm-ergmTerm nodefactor-ergmTerm “kstar” “mm” “nodefactor” nodeifactor-ergmTerm nodematch-ergmTerm nodemix-ergmTerm “nodeifactor” “nodematch” “nodemix” nodeofactor-ergmTerm odegrange-ergmTerm odegree-ergmTerm “nodeofactor” “odegrange” “odegree” odegreeL-ergmTerm ostar-ergmTerm sociality-ergmTerm “odegreeL” “ostar” “sociality” transitiveties-ergmTerm triangle-ergmTerm tripercent-ergmTerm “transitiveties” “triangle” “tripercent” ttriple-ergmTerm “ttriple”
$curved \(curved\)link altkstar-ergmTerm gwb1degree-ergmTerm gwb1degreeL-ergmTerm “altkstar-ergmTerm” “gwb1degree-ergmTerm” “gwb1degreeL-ergmTerm” gwb1dsp-ergmTerm gwb2degree-ergmTerm gwb2degreeL-ergmTerm “gwb1dsp-ergmTerm” “gwb2degree-ergmTerm” “gwb2degreeL-ergmTerm” gwb2dsp-ergmTerm gwdegree-ergmTerm gwdegreeL-ergmTerm “gwb2dsp-ergmTerm” “gwdegree-ergmTerm” “gwdegreeL-ergmTerm” gwdsp-ergmTerm gwesp-ergmTerm gwidegree-ergmTerm “gwdsp-ergmTerm” “gwesp-ergmTerm” “gwidegree-ergmTerm” gwidegreeL-ergmTerm gwnsp-ergmTerm gwodegree-ergmTerm “gwidegreeL-ergmTerm” “gwnsp-ergmTerm” “gwodegree-ergmTerm” gwodegreeL-ergmTerm “gwodegreeL-ergmTerm”
\(curved\)name altkstar-ergmTerm gwb1degree-ergmTerm gwb1degreeL-ergmTerm “altkstar” “gwb1degree” “gwb1degreeL” gwb1dsp-ergmTerm gwb2degree-ergmTerm gwb2degreeL-ergmTerm “gwb1dsp” “gwb2degree” “gwb2degreeL” gwb2dsp-ergmTerm gwdegree-ergmTerm gwdegreeL-ergmTerm “gwb2dsp” “gwdegree” “gwdegreeL” gwdsp-ergmTerm gwesp-ergmTerm gwidegree-ergmTerm “gwdsp” “gwesp” “gwidegree” gwidegreeL-ergmTerm gwnsp-ergmTerm gwodegree-ergmTerm “gwidegreeL” “gwnsp” “gwodegree” gwodegreeL-ergmTerm “gwodegreeL”
$triad-related
\(`triad-related`\)link asymmetric-ergmTerm balance-ergmTerm “asymmetric-ergmTerm” “balance-ergmTerm” ctriple-ergmTerm intransitive-ergmTerm “ctriple-ergmTerm” “intransitive-ergmTerm” localtriangle-ergmTerm nearsimmelian-ergmTerm “localtriangle-ergmTerm” “nearsimmelian-ergmTerm” opentriad-ergmTerm simmelian-ergmTerm “opentriad-ergmTerm” “simmelian-ergmTerm” simmelianties-ergmTerm threetrail-ergmTerm “simmelianties-ergmTerm” “threetrail-ergmTerm” transitive-ergmTerm transitiveties-ergmTerm “transitive-ergmTerm” “transitiveties-ergmTerm” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights-ergmTerm” “triadcensus-ergmTerm” triangle-ergmTerm tripercent-ergmTerm “triangle-ergmTerm” “tripercent-ergmTerm” ttriple-ergmTerm “ttriple-ergmTerm”
\(`triad-related`\)name asymmetric-ergmTerm balance-ergmTerm “asymmetric” “balance” ctriple-ergmTerm intransitive-ergmTerm “ctriple” “intransitive” localtriangle-ergmTerm nearsimmelian-ergmTerm “localtriangle” “nearsimmelian” opentriad-ergmTerm simmelian-ergmTerm “opentriad” “simmelian” simmelianties-ergmTerm threetrail-ergmTerm “simmelianties” “threetrail” transitive-ergmTerm transitiveties-ergmTerm “transitive” “transitiveties” transitiveweights-ergmTerm triadcensus-ergmTerm “transitiveweights” “triadcensus” triangle-ergmTerm tripercent-ergmTerm “triangle” “tripercent” ttriple-ergmTerm “ttriple”
$bipartite \(bipartite\)link b1concurrent-ergmTerm b1cov-ergmTerm b1degrange-ergmTerm “b1concurrent-ergmTerm” “b1cov-ergmTerm” “b1degrange-ergmTerm” b1degree-ergmTerm b1degreeL-ergmTerm b1dsp-ergmTerm “b1degree-ergmTerm” “b1degreeL-ergmTerm” “b1dsp-ergmTerm” b1factor-ergmTerm b1mindegree-ergmTerm b1nodematch-ergmTerm “b1factor-ergmTerm” “b1mindegree-ergmTerm” “b1nodematch-ergmTerm” b1sociality-ergmTerm b1star-ergmTerm b1starmix-ergmTerm “b1sociality-ergmTerm” “b1star-ergmTerm” “b1starmix-ergmTerm” b1twostar-ergmTerm b2concurrent-ergmTerm b2cov-ergmTerm “b1twostar-ergmTerm” “b2concurrent-ergmTerm” “b2cov-ergmTerm” b2degrange-ergmTerm b2degree-ergmTerm b2dsp-ergmTerm “b2degrange-ergmTerm” “b2degree-ergmTerm” “b2dsp-ergmTerm” b2factor-ergmTerm b2mindegree-ergmTerm b2nodematch-ergmTerm “b2factor-ergmTerm” “b2mindegree-ergmTerm” “b2nodematch-ergmTerm” b2sociality-ergmTerm b2star-ergmTerm b2starmix-ergmTerm “b2sociality-ergmTerm” “b2star-ergmTerm” “b2starmix-ergmTerm” b2twostar-ergmTerm coincidence-ergmTerm diff-ergmTerm “b2twostar-ergmTerm” “coincidence-ergmTerm” “diff-ergmTerm” gwb1degree-ergmTerm gwb1degreeL-ergmTerm gwb1dsp-ergmTerm “gwb1degree-ergmTerm” “gwb1degreeL-ergmTerm” “gwb1dsp-ergmTerm” gwb2degree-ergmTerm gwb2degreeL-ergmTerm gwb2dsp-ergmTerm “gwb2degree-ergmTerm” “gwb2degreeL-ergmTerm” “gwb2dsp-ergmTerm” isolatededges-ergmTerm “isolatededges-ergmTerm”
\(bipartite\)name b1concurrent-ergmTerm b1cov-ergmTerm b1degrange-ergmTerm “b1concurrent” “b1cov” “b1degrange” b1degree-ergmTerm b1degreeL-ergmTerm b1dsp-ergmTerm “b1degree” “b1degreeL” “b1dsp” b1factor-ergmTerm b1mindegree-ergmTerm b1nodematch-ergmTerm “b1factor” “b1mindegree” “b1nodematch” b1sociality-ergmTerm b1star-ergmTerm b1starmix-ergmTerm “b1sociality” “b1star” “b1starmix” b1twostar-ergmTerm b2concurrent-ergmTerm b2cov-ergmTerm “b1twostar” “b2concurrent” “b2cov” b2degrange-ergmTerm b2degree-ergmTerm b2dsp-ergmTerm “b2degrange” “b2degree” “b2dsp” b2factor-ergmTerm b2mindegree-ergmTerm b2nodematch-ergmTerm “b2factor” “b2mindegree” “b2nodematch” b2sociality-ergmTerm b2star-ergmTerm b2starmix-ergmTerm “b2sociality” “b2star” “b2starmix” b2twostar-ergmTerm coincidence-ergmTerm diff-ergmTerm “b2twostar” “coincidence” “diff” gwb1degree-ergmTerm gwb1degreeL-ergmTerm gwb1dsp-ergmTerm “gwb1degree” “gwb1degreeL” “gwb1dsp” gwb2degree-ergmTerm gwb2degreeL-ergmTerm gwb2dsp-ergmTerm “gwb2degree” “gwb2degreeL” “gwb2dsp” isolatededges-ergmTerm “isolatededges”
$frequently-used
\(`frequently-used`\)link b1cov-ergmTerm b1degree-ergmTerm b1degreeL-ergmTerm “b1cov-ergmTerm” “b1degree-ergmTerm” “b1degreeL-ergmTerm” b1factor-ergmTerm b1nodematch-ergmTerm b2concurrent-ergmTerm “b1factor-ergmTerm” “b1nodematch-ergmTerm” “b2concurrent-ergmTerm” b2cov-ergmTerm b2degree-ergmTerm b2factor-ergmTerm “b2cov-ergmTerm” “b2degree-ergmTerm” “b2factor-ergmTerm” b2nodematch-ergmTerm degree-ergmTerm degreeL-ergmTerm “b2nodematch-ergmTerm” “degree-ergmTerm” “degreeL-ergmTerm” diff-ergmTerm edgecov-ergmTerm gwdegree-ergmTerm “diff-ergmTerm” “edgecov-ergmTerm” “gwdegree-ergmTerm” gwdegreeL-ergmTerm gwesp-ergmTerm idegree-ergmTerm “gwdegreeL-ergmTerm” “gwesp-ergmTerm” “idegree-ergmTerm” idegreeL-ergmTerm isolates-ergmTerm mm-ergmTerm “idegreeL-ergmTerm” “isolates-ergmTerm” “mm-ergmTerm” mutual-ergmTerm mutualL-ergmTerm nodecov-ergmTerm “mutual-ergmTerm” “mutualL-ergmTerm” “nodecov-ergmTerm” nodefactor-ergmTerm nodeicov-ergmTerm nodeifactor-ergmTerm “nodefactor-ergmTerm” “nodeicov-ergmTerm” “nodeifactor-ergmTerm” nodematch-ergmTerm nodemix-ergmTerm odegree-ergmTerm “nodematch-ergmTerm” “nodemix-ergmTerm” “odegree-ergmTerm” odegreeL-ergmTerm triangle-ergmTerm “odegreeL-ergmTerm” “triangle-ergmTerm”
\(`frequently-used`\)name b1cov-ergmTerm b1degree-ergmTerm b1degreeL-ergmTerm “b1cov” “b1degree” “b1degreeL” b1factor-ergmTerm b1nodematch-ergmTerm b2concurrent-ergmTerm “b1factor” “b1nodematch” “b2concurrent” b2cov-ergmTerm b2degree-ergmTerm b2factor-ergmTerm “b2cov” “b2degree” “b2factor” b2nodematch-ergmTerm degree-ergmTerm degreeL-ergmTerm “b2nodematch” “degree” “degreeL” diff-ergmTerm edgecov-ergmTerm gwdegree-ergmTerm “diff” “edgecov” “gwdegree” gwdegreeL-ergmTerm gwesp-ergmTerm idegree-ergmTerm “gwdegreeL” “gwesp” “idegree” idegreeL-ergmTerm isolates-ergmTerm mm-ergmTerm “idegreeL” “isolates” “mm” mutual-ergmTerm mutualL-ergmTerm nodecov-ergmTerm “mutual” “mutualL” “nodecov” nodefactor-ergmTerm nodeicov-ergmTerm nodeifactor-ergmTerm “nodefactor” “nodeicov” “nodeifactor” nodematch-ergmTerm nodemix-ergmTerm odegree-ergmTerm “nodematch” “nodemix” “odegree” odegreeL-ergmTerm triangle-ergmTerm “odegreeL” “triangle”
$non-negative
\(`non-negative`\)link cyclicalweights-ergmTerm transitiveweights-ergmTerm “cyclicalweights-ergmTerm” “transitiveweights-ergmTerm”
\(`non-negative`\)name cyclicalweights-ergmTerm transitiveweights-ergmTerm “cyclicalweights” “transitiveweights”
This documentation was built with..
## R version 4.2.1 (2022-06-23)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.2.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ergm_4.1-6674 network_1.18.0
##
## loaded via a namespace (and not attached):
## [1] DEoptimR_1.0-11 bslib_0.4.2 compiler_4.2.1
## [4] pillar_1.8.1 jquerylib_0.1.4 tools_4.2.1
## [7] digest_0.6.31 jsonlite_1.8.4 evaluate_0.19
## [10] memoise_2.0.1 lifecycle_1.0.3 tibble_3.1.8
## [13] rle_0.9.2 lattice_0.20-45 pkgconfig_2.0.3
## [16] rlang_1.0.6 Matrix_1.5-3 cli_3.4.1
## [19] yaml_2.3.6 parallel_4.2.1 xfun_0.35
## [22] fastmap_1.1.0 coda_0.19-4 stringr_1.5.0
## [25] knitr_1.41 vctrs_0.5.1 sass_0.4.4
## [28] trust_0.1-8 grid_4.2.1 glue_1.6.2
## [31] robustbase_0.95-0 R6_2.5.1 fansi_1.0.3
## [34] rmarkdown_2.19 purrr_0.3.5 magrittr_2.0.3
## [37] htmltools_0.5.4 MASS_7.3-58.1 utf8_1.2.2
## [40] stringi_1.7.8 lpSolveAPI_5.5.2.0-17.9 cachem_1.0.6
## [43] statnet.common_4.8.0