testthat, knitr, rmarkdown, gridExtra, Heatplus
ggplot2, Matrix, methods, qtl, igraph, fastcluster, pryr,
nnls, RColorBrewer, jsonlite, progress, stats, sn, car

Constructing linkage maps, reconstructing haplotypes,
estimating linkage disequilibrium and quantitative trait loci
(QTL) mapping in multi-parent Recombinant Inbred Lines designs.

Daniel Hernando Rodriguez <dhrodriguezt@unal.edu.co> & Campo
Elias Pardo <cepardot@unal.edu.co>

Zhu Wang, with contributions from Achim Zeileis, Simon Jackman, Brian Ripley, Trevor Hastie, Rob Tibshirani, Balasubramanian Narasimhan, Gil Chu and Patrick Breheny
Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.

Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>,
Wang et al. (2016) <doi:10.1177/0962280214530608>.

MASS, pscl, numDeriv, foreach, doParallel, bst

Algorithms optimize penalized models. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>,
Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2019) <arXiv:1912.11119>.