Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).

Contains functions for fitting shared frailty models with a semi-parametric
    baseline hazard with the Expectation-Maximization algorithm. Supported data formats 
    include clustered failures with left truncation and recurrent events in gap-time
    or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable
    and the Power Variance Family are supported.

Implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

The penalized and non-penalized Minorize-Maximization (MM) method for frailty models to fit the clustered data, multi-event data and recurrent data. Least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalized functions are implemented. All the methods are computationally efficient. These general methods are proposed based on the following papers,
    Huang, Xu and Zhou (2022) <doi:10.3390/math10040538>,
    Huang, Xu and Zhou (2023) <doi:10.1177/09622802221133554>.

Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.

Tools to support sensible statistics for functional response analysis.