jmcm: a Python package for analyzing longitudinal data using joint mean-covariance models

Abstract

The joint mean-covariance models are widely used in longitudinal studies. To draw the mean and covariance patterns of data, joint modeling based on the normality assumptions with the modified Cholesky decompositions (MCD) has been proposed. The method keeps the positive-definiteness and also has advantages in interpreting the statistical roles of the parameters. We proposed a Python package named jmcm to fit the mean-covariance models. It contains the methods for estimating the parameters based on the profile likelihood estimation, plotting the curves with confidence bands based on bootstrap method, doing hypothesis tests based on the Wald tests, etc. This article would review the methods and demonstrate the use of the package with detailed examples. The compatibility of the package would also be included based on simulation study.

Keywords:

• Hypothesis tests
• Joint mean-covariance models
• Likelihood estimations
• Modified Cholesky decompositions
• Python

MATHEMATICS SUBJECT CLASSIFICATION:

• MSC62H12; MSC62H20