A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.

In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net <doi: 10.32614/RJ-2021-010>.

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) <doi:10.4310/SII.2013.v6.n2.a1>.

Implements fast change point detection algorithm based on the
    paper "Sequential Gradient Descent and Quasi-Newton's Method for
    Change-Point Analysis" by Xianyang Zhang, Trisha Dawn
    <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is
    based on dynamic programming with pruning and sequential gradient
    descent. It is able to detect change points a magnitude faster than
    the vanilla Pruned Exact Linear Time(PELT). The package includes
    examples of linear regression, logistic regression, Poisson
    regression, penalized linear regression data, and whole lot more
    examples with custom cost function in case the user wants to use their
    own cost function.

For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of
    CUB models (where CUB stands for Combination of a 
	discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis' identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view.