Title: Bayesian Inference for Neyman-Scott Point Processes
person("Mrkvicka", "Tomas", email = "mrkvicka.toma@gmail.com", role = "aut"),
person("Dvorak","Jiri", email = "dvorak@karlin.mff.cuni.cz", role = "aut"),
person("Beranek", "Ladislav", email = "beranek@jcu.cz", role = c("aut")),
person("Remes","Radim", email = "inrem@jcu.cz", role = c("aut", "cre"))
Description: 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>.
Imports: VGAM, cluster, mvtnorm, spatstat, spatstat.core,

Imports: Rcpp, VGAM, cluster, mvtnorm, spatstat, spatstat.core,

Title: Binscatter Estimation and Inference
Author: Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Yingjie Feng
Description: Provides tools for statistical analysis using the binscatter methods developed by Cattaneo, Crump, Farrell and Feng (2019a) <arXiv:1902.09608> and Cattaneo, Crump, Farrell and Feng (2019b) <arXiv:1902.09615>. Binscatter provides a flexible way of describing the mean relationship between two variables based on partitioning/binning of the independent variable of interest. binsreg() implements binscatter estimation and robust (pointwise and uniform) inference of regression functions and derivatives thereof, with particular focus on constructing binned scatter plots. binsregtest() implements hypothesis testing procedures for parametric functional forms of and nonparametric shape restrictions on the regression function. 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.