Description: Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to Liang, F., Li, Q. and Zhou, L. (2017) at <doi:10.1080/01621459.2017.1409122> for more detail.

Authors@R: c(person("Bochao", "Jia", role = c("ctb", "aut", "cre", "cph"), email = "jbc409@ufl.edu"),
Description: Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to <doi:10.1080/01621459.2017.1409122> for more detail.
Author: Bochao Jia [ctb, aut, cre, cph],

Title: Bayesian Nonparametric Model for Clustering with Mixed Scale
Authors@R: c(person("Christian", "Carmona", role = c("aut", "cre"), email = "carmona@stats.ox.ac.uk"),
person("Luis", "Nieto-Barajas", role = "aut", email = " lnieto@itam.mx"),
person("Antonio", "Canale", role = "ctb", email = "antonio.canale@unito.it")
Description: Bayesian nonparametric approach for clustering that is capable to combine different types of variables (continuous, ordinal and nominal) and also accommodates for different sampling probabilities in a complex survey design. The model is based on a location mixture model with a Poisson-Dirichlet process prior on the location parameters of the associated latent variables. The package performs the clustering model described in Carmona, C., Nieto-Barajas, L. E., Canale, A. (2016) <http://arxiv.org/abs/1612.00083>.
Imports: matrixcalc, truncnorm, mvtnorm, plyr, MASS

Imports: matrixcalc, truncnorm, mvtnorm, plyr, MASS, compiler

Description: Bayesian nonparametric approach for clustering that is capable to combine different types of variables (continuous, ordinal and nominal) and also accommodates for different sampling probabilities in a complex survey design. The model is based on a location mixture model with a Poisson-Dirichlet process prior on the location parameters of the associated latent variables. The package performs the clustering model described in Carmona, C., Nieto-Barajas, L. E., Canale, A. (2016) <arXiv:1612.00083>.
Imports: Rcpp (>= 0.12.12), matrixcalc, truncnorm, mvtnorm, plyr, MASS,