Implements operations for Riemannian manifolds, e.g., geodesic distance, Riemannian metric, exponential and logarithm maps, etc. Also incorporates random object generator on the manifolds. See Dai, Lin, and Müller (2021) <doi:10.1111/biom.13385>.

An R interface to version 0.3 of the 'ROPTLIB' optimization library
    (see <https://www.math.fsu.edu/~whuang2/> for more information). Optimize real-
    valued functions over manifolds such as Stiefel, Grassmann, and Symmetric
    Positive Definite matrices. For details see Martin et. al. (2020) <doi:10.18637/jss.v093.i01>. 
    Note that the optional ldr package used in some of this package's examples can be obtained from either JSS 
    <https://www.jstatsoft.org/index.php/jss/article/view/v061i03/2886> or from the CRAN archives 
    <https://cran.r-project.org/src/contrib/Archive/ldr/ldr_1.3.3.tar.gz>.

Interactive plotting functions for use within RStudio.
  The manipulate function accepts a plotting expression and a set of
  controls (e.g. slider, picker, checkbox, or button) which are used
  to dynamically change values within the expression. When a value is
  changed using its corresponding control the expression is
  automatically re-executed and the plot is redrawn.

Like package 'manipulate' does for static graphics, this package
    helps to easily add controls like sliders, pickers, checkboxes, etc. that 
    can be used to modify the input data or the parameters of an interactive 
    chart created with package 'htmlwidgets'.

The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.

Chromatin immunoprecipitation followed by high-throughput
    sequencing (ChIP-seq) is the premier technology for profiling genome-wide
    localization of chromatin-binding proteins, including transcription
    factors and histones with various modifications.
    This package provides a robust method for normalizing ChIP-seq
    signals across individual samples or groups of samples. It also designs
    a self-contained system of statistical models for calling differential
    ChIP-seq signals between two or more biological conditions as well as
    for calling hypervariable ChIP-seq signals across samples. Refer to
    Tu et al. (2021) <doi:10.1101/gr.262675.120> and
    Chen et al. (2022) <doi:10.1186/s13059-022-02627-9>
    for associated statistical details.