Latent Class Trajectory Models: An R Package
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Updated
Dec 1, 2022 - R
Latent Class Trajectory Models: An R Package
📦 Non-parametric Causal Effects Based on Modified Treatment Policies 🔮
R package for fitting joint models to time-to-event data and multivariate longitudinal data
Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood
R package for fitting joint models to time-to-event and longitudinal data
An R package for clustering longitudinal datasets in a standardized way, providing interfaces to various R packages for longitudinal clustering, and facilitating the rapid implementation and evaluation of new methods
Implementation of Gaussian Process Panel Modeling in R
R-package for relational event models (one- and two-mode networks)
Raw files for a document simulating models for longitudinal data (mixed models and growth curve models).
Broken Stick Model for Irregular Longitudinal Data
Track, Analyze, Visualize: Unravel Your Microbiome's Temporal Pattern with MicrobiomeStat
R/medltmle: Estimation and Inference for Natural Mediation Effect in Longitudinal Data
Clustering longitudinal data with potential sparse, irregular observations, multiple outcomes, and unbalanced cluster sizes
An R data package for the book "Applied longitudinal data analysis: Modeling change and event occurrence" by Singer and Willett (2003).
optic: Simulation Tool for Causal Inference Using Longitudinal Data
This repository contains code to estimate sample size needed to compare dynamic treatment regimens using longitudinal count outcomes from a Sequential Multiple Assignment Randomized Trial (SMART).
A Shiny interface for the MicrobiomeStat R package, designed to facilitate analysis and visualization of microbiome data.
R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.
MVLS v1.1 is a function for R software to impute missing values in longitudinal dataset. R package.
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