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JLPM (version 1.0.2)

Joint Latent Process Models

Description

Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 .

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Version

Install

install.packages('JLPM')

Monthly Downloads

213

Version

1.0.2

License

GPL (>= 2.0)

Maintainer

Viviane Philipps

Last Published

October 6th, 2023

Functions in JLPM (1.0.2)

summary.jointLPM

Summary of a joint latent process model
StandardMethods

Standard methods for estimated models
JLPM-package

Estimation of joint latent process models
print.jointLPM

Brief summary of a joint latent process model
convert

Conversion
jointLPM

Estimation of latent process joint models for multivariate longitudinal outcomes and time-to-event data.