Group Based Modeling Trajectory

Estimation of group-based trajectory models, including finite mixture models for longitudinal data, supporting censored normal, zero-inflated Poisson, logit, and beta distributions, using expectation-maximization and quasi-Newton methods, with tools for model selection, diagnostics, and visualization of latent trajectory groups, , Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press. and Noel (2022), < https://orbilu.uni.lu/>, thesis.

![Logo](man/figures/logotrajeR.png){width=20%}

A R package that fit regression mixture model - group-based trajectory modeling (GBTM).

Longitudinal studies are often employed on several disciplines like finance, econometrics, psychology, sociology, biology, ...

The aims of this package is to give tools to work with this situation. On the one hand by divide data in some clusters and on the other hand by fitting longitudinal trajectories in each clusters.

Package Functionality

TrajeR support several distributions

Censored (or regular) Normal distribution ;
Zero Inflated Poisson distribution ;
Poisson distribution ;
Bernouilli distribution ;
Beta distribution (only withe Likelihood) ;
Non linear regression.

The trajectories of each clusters are modeling by polynomials. We have the possibility to use Non Linear models too.

Installation

To get the current released version from CRAN.

To get the current development version from Codeberg:

# Install from the git repository
if (!require("devtools")) install.packages("devtools")
devtools::install_git(url = "https://codeberg.org/cidric/trajeR")

Usage

library(trajeR)

The main function of the package is

trajeR(Y, A, X = NULL, TCOV = NULL, ng, degre, degre.nu = 0, 
       Model, Method = "L", 
       ssigma = FALSE, ymax = max(Y) + 1, ymin = min(Y) - 1,
       hessian = TRUE, itermax = 100, paraminit = NULL, 
       EMIRLS = TRUE, refgr = 1,
       fct = NULL, diffct = NULL, nbvar = NULL, nls.lmiter)

For more details about the usage of this package you can read the vignette documentation (coming soon).