Uniform random samples from simple manifolds, sometimes with noise,
    are commonly used to test topological data analytic (TDA) tools.
    This package includes samplers powered by two techniques: analytic
    volume-preserving parameterizations, as employed by Arvo (1995)
    <doi:10.1145/218380.218500>, and rejection sampling, as employed by
    Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.

Provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, 
    all code for the vector summaries is written in 'C++' using the 'Rcpp' and 'RcppArmadillo' packages.

The companion package that provides all the datasets used in the book
 "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).

An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the 'gbm' package originally developed by Greg Ridgeway.

Estimate the transition diagnostic classification model (TDCM) 
    described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a 
    longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in 
    Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM 
    subsumes many other diagnostic classification models (DCMs), many other DCMs 
    can be estimated longitudinally via the TDCM. The 'TDCM' package includes 
    functions to estimate the single-group and multigroup TDCM, summarize 
    results of interest including item parameters, growth proportions, 
    transition probabilities, transitional reliability, attribute correlations, 
    model fit, and growth plots.

A collection of functions for automatically creating 'Stan'
      code for transition diagnostic classification models (TDCMs) as they are
      defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>.
      This package supports automating the creation of 'Stan' code for TDCMs,
      fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal
      across all items), and multi-threaded TDCMs.