Description: Likelihood-based estimation and data generation from a class of models used to estimate peer effects on count data by controlling for the network endogeneity. This class includes count data models with social interactions (Houndetoungan 2022; <doi:10.2139/ssrn.3721250>), spatial tobit models (Xu and Lee 2015; <doi:10.1016/j.jeconom.2015.05.004>), and spatial linear-in-means models (Lee 2004; <doi:10.1111/j.1468-0262.2004.00558.x>).
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppDist
Suggests: ggplot2, MASS, knitr, rmarkdown

LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppDist, RcppNumerical,

Authors@R: person("Elysee Aristide", "Houndetoungan", role = c("cre","aut"), email =  "ariel92and@gmail.com")
Author: Elysee Aristide Houndetoungan [cre, aut]
Maintainer: Elysee Aristide Houndetoungan <ariel92and@gmail.com>

Title: Cost-Effectiveness Analysis using Markov Models
Description: Provides an accurate, fast and easy way to perform cost-effectiveness analyses.
This package can be used to validate results generated using different methods and can
help create a standard for cost-effectiveness analyses, that will help compare results

Author: Konrad Kamieniecki [aut, cre], Przemyslaw Spurek [ctb]
Description: Cross-Entropy Clustering (CEC) divides the data into Gaussian type clusters. It performs the automatic reduction of unnecessary clusters, while at the same time allows the simultaneous use of various type Gaussian mixture models.
Imports: graphics, methods, stats, utils

Description: CEC divides data into Gaussian type clusters. The implementation allows the simultaneous use of various type Gaussian mixture models, performs the reduction of unnecessary clusters and it's able to discover new groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.

Description: Clustering that splits data into Gaussian type clusters. The implementation allows the simultaneous use of various type Gaussian mixture models, performs the reduction of unnecessary clusters and it's able to discover new groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.