Calculates metrics of proportionality using the logit-normal multinomial model.  It can also provide empirical and plugin estimates of these metrics.

Flexible univariate count models based on renewal processes. The
    models may include covariates and can be specified with familiar formula
    syntax as in glm() and package 'flexsurv'.  The methodology is described by
    Kharrat et all (2019) <doi:10.18637/jss.v090.i13> (included as vignette
    'Countr_guide' in the package).
    If the suggested package 'pscl' is not available from CRAN, it can be
    installed with 'remotes::install_github("cran/pscl")'. It is no longer used
    by the functions in this package but is needed for some of the extended
    examples.

Wrangle country data more effectively and quickly. 
    This package contains functions to easily identify and convert country names,
    download country information, merge country data from different sources, 
    and make quick world maps.

Standardize country names, convert them into one of 40
    different coding schemes, convert between coding schemes, and assign
    region descriptors.

Modeling under- and over-dispersed count data using extended Poisson process models as in the article Faddy and Smith (2011) <doi:10.18637/jss.v069.i06> .

Implements the count splitting methodology from Neufeld et al. (2022) <doi:10.1093/biostatistics/kxac047> and Neufeld et al. (2023) <arXiv:2307.12985>. Intended for turning a matrix of single-cell RNA sequencing counts, or similar count datasets, into independent folds that can be used for training/testing or cross validation. Assumes that the entries in the matrix are from a Poisson or a negative binomial distribution.

For Bayesian and classical inference and prediction with count-valued data,
    Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable,
    and easy-to-use approach. STAR models the observed count data using a rounded 
    continuous data model and incorporates a transformation for greater flexibility.
    Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of 
    (i) transforming count-valued data and subsequently 
    (ii) modeling the transformed data using Gaussian models. 
    STAR is well-defined for count-valued data, which is reflected in predictive accuracy, 
    and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. 
    Importantly, STAR is easy to combine with existing MCMC or point estimation
    methods for continuous data, which allows seamless adaptation of continuous data
    models (such as linear regressions, additive models, BART, random forests,
    and gradient boosting machines) for count-valued data. The package also includes several
    methods for modeling count time series data, namely via warped Dynamic Linear Models. 
    For more details and background on these methodologies, see the works of 
    Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, 
    Kowal and Wu (2022) <doi:10.1111/biom.13617>, 
    King and Kowal (2022) <arXiv:2110.14790>, and 
    Kowal and Wu (2023) <arXiv:2110.12316>.