JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1183 10.6339/25-JDS1183 Computing in Data Science The Journey to Improve LCPM: An R Package for Ordinal Regression DePrattiRolandroland.depratti@my.ccsu.edu1∗ SinghGurbakhshash1 Central Connecticut State University, New Britain, Connecticut, USA Corresponding author. Email: roland.depratti@my.ccsu.edu. 2025552025232399415 Supplementary Material

The supplementary zip file contains all source code (both R and CPP), an R package used to run test case D, a sample windows batch script to run the code and 3 sample csv files that are input into the batch file. There is also a readme file included with more explanation on how to use these files.

3102024442025 2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2025 Open access article under the CC BY license.

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.

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