The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function are included in this package. The maximization process is carried out using Brent's algorithm, with the 'C++' code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).

This package provides functions for the estimation of
latent class mixed models using a maximum likelihood method

Estimation of various latent class mixed models and joint latent
class mixed models
Cecile Proust-Lima, Amadou Diakite and Benoit Liquet
This package provides functions for the estimation of
various latent class mixed models and joint latent latent class
mixed models using a maximum likelihood method

Estimation of various latent class mixed models, joint latent
class mixed models and mixed models for curvilinear outcomes
This package provides functions for the estimation of
various latent class mixed models, joint latent latent class
mixed models and mixed models for curvilinear outcomes using a
maximum likelihood method

Estimation of latent class mixed models, joint latent class
mixed models and mixed models for curvilinear outcomes

Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit
Liquet

Estimation of extended mixed models using latent classes and
latent processes
Cecile Proust-Lima, Viviane Philipps, Amadou Diakite and Benoit Liquet
This package provides functions for the estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.