Statistical analysis with missing data
Statistical analysis with missing data
Semi-parametric marginal models for hierarchical data and their corresponding full models
Computational Statistics & Data Analysis
Fitting semiparametric regressions for panel count survival data with an R package spef
Computer Methods and Programs in Biomedicine
Kml: A package to cluster longitudinal data
Computer Methods and Programs in Biomedicine
A warning concerning the estimation of multinomial logistic models with correlated responses in SAS
Computer Methods and Programs in Biomedicine
KmL3D: A non-parametric algorithm for clustering joint trajectories
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.