cold: An R Package for the Analysis of Count Longitudinal Data

Abstract

This paper describes the R package cold for the analysis of count longitudinal data. In this package marginal and random effects models are considered. In both cases estimation is via maximization of the exact likelihood and serial dependence among observations is assumed to be of Markovian type and referred as the integer-valued autoregressive of order one process. For random effects models adaptive Gaussian quadrature and Monte Carlo methods are used to compute integrals whose dimension depends on the structure of random effects. cold is written partly in R language, partly in Fortran 77, interfaced through R and is built following the S4 formulation of R methods.

LongDat: an R package for covariate-sensitive longitudinal analysis of high-dimensional data

“…differences in microbial abundance levels between individuals) should be addressed. Several tools dealing with longitudinal microbiome data already exist, and some allow taking in covariates for analysis ( Asar et al , 2013 ; Chen et al , 2016 ; Gonçalves et al , 2021 ; Mallick et al , 2021 ; Mandal et al , 2015 ; Opgen-Rhein et al , 2021 ; Timonen et al , 2021 ). Nevertheless, none of these tools reports explicitly on how the effect of time variable is affected by the presence of other covariates while detecting and simultaneously controlling for them.…”