The Bayesian MCMC estimation of parameters for Thomas-type cluster
    point process with various inhomogeneities. It allows for inhomogeneity in
    (i) distribution of parent points, (ii) mean number of points in a cluster,
    (iii) cluster spread. The package also allows for the Bayesian MCMC
    algorithm for the homogeneous generalized Thomas process. The cluster size
    is allowed to have a variance that is greater or less than the expected
    value (cluster sizes are over or under dispersed). Details are described in
    Dvořák, Remeš, Beránek & Mrkvička (2022) <arXiv: 10.48550/arXiv.2205.07946>.

Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2024a) <doi:10.48550/arXiv.1902.09608>, Cattaneo, Crump, Farrell and Feng (2024b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2024_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2024c) <doi:10.48550/arXiv.1902.09615>. Binscatter provides a flexible way of describing the relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg(), binsqreg() and binsglm() implement binscatter least squares regression, quantile regression and generalized linear regression respectively, with particular focus on constructing binned scatter plots. They also implement robust (pointwise and uniform) inference of regression functions and derivatives thereof. binstest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. binspwc() implements hypothesis testing procedures for pairwise group comparison of binscatter estimators. binsregselect() implements data-driven procedures for selecting the number of bins for binscatter estimation. All the commands allow for covariate adjustment, smoothness restrictions and clustering.

Various supervised and unsupervised binning tools
    including using entropy, recursive partition methods
    and clustering.