Authors@R: c( person("Pauline", "Adamopoulou", role = c("aut",
Author: Pauline Adamopoulou [aut, cre, cph],

Title: Modular Breeding Program Simulator
Description: Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al (2019) <doi:10.1101/829333>.
Suggests: EMMREML, BGLR, MASS, doMPI, doRNG, compiler, foreach, sommer,
vcfR, jsonlite, rrBLUP, biomaRt, Matrix, doParallel
Enhances: miraculix (>= 0.9.10), RandomFieldsUtils (>= 0.5.9),

Description: Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. The package is also used as the background simulator for our a web-based interface <http:www.mobps.de>. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.
vcfR, jsonlite, rrBLUP, biomaRt, Matrix, doParallel,
RColorBrewer, optiSel, alstructure, NAM, gplots, phylogram

RColorBrewer, optiSel, alstructure, NAM, gplots, phylogram,

Title: Parsimonious Model-Based Clustering with Covariates
person("Thomas Brendan", "Murphy", email = "brendan.murphy@ucd.ie", role = "ctb"))
Description: Clustering via parsimonious Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2017) <arXiv:1711.05632>. This package fits finite Gaussian mixture models with gating and expert network covariates using parsimonious covariance parameterisations from 'mclust' via the EM algorithm. Visualisation of the results of such models using generalised pairs plots is also facilitated.
Imports: grid, lattice, matrixStats, mclust (>= 5.1), mvnfast, nnet,
Suggests: cluster, geometry, knitr, rmarkdown

Imports: lattice, matrixStats, mclust (>= 5.1), mvnfast, nnet, vcd

Title: Gaussian Parsimonious Clustering Models with Covariates
Description: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2017) <arXiv:1711.05632>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations via the EM algorithm. Visualisation of the results of such models using generalised pairs plots is also facilitated.
Suggests: cluster, clustMD, geometry, knitr, rmarkdown