Provides several methods for generating density functions
    based on binned data. Methods include step function, recursive
    subdivision, and optimized spline. Data are assumed to be nonnegative, 
    the top bin is assumed to have no upper bound, but the bin widths need
    be equal. All PDF smoothing methods maintain the areas specified by 
    the binned data. (Equivalently, all CDF smoothing methods interpolate 
    the points specified by the binned data.) In practice, an estimate for 
    the mean of the distribution should be supplied as an optional argument.
    Doing so greatly improves the reliability of statistics computed from 
    the smoothed density functions. Includes methods for estimating the Gini 
    coefficient, the Theil index, percentiles, and random deviates from a 
    smoothed distribution. Among the three methods, the optimized spline 
    (splinebins) is recommended for most purposes. The percentile and 
    random-draw methods should be regarded as experimental, and these methods 
    only support splinebins.

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 (2023a) <arXiv:1902.09608>, Cattaneo, Crump, Farrell and Feng (2023b) <https://nppackages.github.io/references/Cattaneo-Crump-Farrell-Feng_2023_NonlinearBinscatter.pdf> and Cattaneo, Crump, Farrell and Feng (2023c) <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.