Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.

Methods for estimating time-varying infectious disease
	transmission rates from disease incidence time series, based
	on discretizations of an SIR model, as analyzed in Jagan et al.
	(2020) <doi:10.1371/journal.pcbi.1008124>.

This is a two-in-one package which provides interfaces to
        both R and 'Python'. It implements fast hierarchical, agglomerative
        clustering routines. Part of the functionality is designed as drop-in
        replacement for existing routines: linkage() in the 'SciPy' package
        'scipy.cluster.hierarchy', hclust() in R's 'stats' package, and the
        'flashClust' package. It provides the same functionality with the
        benefit of a much faster implementation. Moreover, there are
        memory-saving routines for clustering of vector data, which go beyond
        what the existing packages provide. For information on how to install
        the 'Python' files, see the file INSTALL in the source distribution.
        Based on the present package, Christoph Dalitz also wrote a pure 'C++'
        interface to 'fastcluster':
        <https://lionel.kr.hs-niederrhein.de/~dalitz/data/hclust/>.

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.

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: <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.