Journal of Data Science

Recently, the log cumulative probability model (LCPM) and its special case the proportional probability model (PPM) was developed to relate ordinal outcomes to predictor variables using the log link instead of the logit link. These models permit the estimation of probability instead of odds, but the log link requires constrained maximum likelihood estimation (cMLE). An algorithm that efficiently handles cMLE for the LCPM is a valuable resource as these models are applicable in many settings and its output is easy to interpret. One such implementation is in the R package lcpm. In this era of big data, all statistical models are under pressure to meet the new processing demands. This work aimed to improve the algorithm in R package lcpm to process more input in less time using less memory.