JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1020 10.6339/21-JDS1020 Computing in Data Science Shape-Restricted Regression Splines with R Package splines2 https://orcid.org/0000-0003-0363-3180 WangWenjiewang@wwenjie.org1∗ https://orcid.org/0000-0003-4401-7296 YanJun2 Eli Lilly and Company, Indianapolis, Indiana, USA Department of Statistics, University of Connecticut, Storrs, Connecticut, USA Corresponding Author. Email: wang@wwenjie.org. 20211282021193498517 Supplementary Material

We review the generalized Bernstein polynomials, B-splines, and Natural cubic splines implemented in the package splines2 but not covered in the main text. We also provide the R code that produced the micro-benchmark results.

20720212072021 2021 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2021 Open access article under the CC BY license.

Splines are important tools for the flexible modeling of curves and surfaces in regression analyses. Functions for constructing spline basis functions are available in R through the base package splines. When the curves to be modeled have known characteristics in monotonicity or curvature, more efficient statistical inferences are possible with shape-restricted splines. Such splines, however, are not available in the R package splines. The package splines2 provides easy-to-use shape-restricted spline basis functions, along with their derivatives and integrals which are important tools in many inference scenarios. It also provides additional splines and features that are not available in the splines package, such as periodic splines and generalized Bernstein polynomials. The usages of the functions are illustrated with shape-restricted regression, recurrent event data analysis, and extreme-value copulas.

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