Poisson disk sampling is a method of generating blue noise 
    sample patterns where all samples are at least a specified distance 
    apart. Poisson samples may be generated in two or three dimensions 
    with this package. The algorithm used is an 
    implementation of  Bridson's "Fast Poisson disk sampling in 
    arbitrary dimensions" <doi:10.1145%2F1278780.1278807>.

Implementation of the exact, normal approximation, and simulation-based methods for computing the probability mass function (pmf) and cumulative distribution function (cdf) of the Poisson-Multinomial distribution, together with a random number generator for the distribution. The exact method is based on multi-dimensional fast Fourier transformation (FFT) of the characteristic function of the Poisson-Multinomial distribution. The normal approximation method uses a multivariate normal distribution to approximate the pmf of the distribution based on central limit theorem. The simulation method is based on the law of large numbers. Details about the methods are available in Lin, Wang, and Hong (2022) <DOI:10.1007/s00180-022-01299-0>.

For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.

Bindings for Poisson regression models for use with the
    'parsnip' package. Models include simple generalized linear models,
    Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber,
    and Jackman (2008) <doi:10.18637/jss.v027.i08>).

Provides a dataset of Pokemon information in 
  both English and Brazilian Portuguese. The dataset contains 949 rows and 
  22 columns, including information such as the Pokemon's name, ID, height, 
  weight, stats, type, and more.

Distributes data from the Polarization in Comparative Attitudes Project. Helper
    functions enable data retrieval in wide and tidy formats for user-defined
    countries and years. Provides support for case-insensitive country names in many
    languages. Mehlhaff (2022) <https://imehlhaff.net/files/Polarization%20and%20Democracy.pdf>.