Coordinate-Descent Algorithm for Discrete Data
person("Jiaying", "Gu", email = "gujy.lola@gmail.com", role = c("aut", "cre"))
Structure learning of Bayesian network using coordinate-descent
algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
and we use a multi-logit model to do the regression.
The algorithm is described in Gu, Fu and Zhou (2016) <http://arxiv.org/abs/1403.2310>.

Rcpp (>= 0.11.0), sparsebnUtils (>= 0.0.4), igraph

Coordinate-Descent Algorithm for Learning Sparse Discrete
Bayesian Networks
Structure learning of Bayesian network using coordinate-descent
algorithm. This algorithm is designed for discrete network assuming a multinomial data set,
and we use a multi-logit model to do the regression.
The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.

Simulated Goodness-of-Fit Tests for Discrete Distributions
person(given = "Josh",
family = "McCormick",
role = c("aut", "cre"),
email = "josh.mccormick@aya.yale.edu")
Implements fast, Monte Carlo simulations for
goodness-of-fit (GOF) tests for discrete distributions. This
includes tests based on the root-mean-square statistic, the
Chi-squared statistic, the log-likelihood-ratio statistic (G^2), and
the Kolmogorov-Smirnov statistic.
knitr, dgof, RVAideMemoire, bench, testthat (>= 3.0.0),
rmarkdown

Implements Monte Carlo simulations for
goodness-of-fit (GOF) tests for discrete distributions. This
includes tests based on the Chi-squared statistic, the
log-likelihood-ratio (G^2) statistic, the Freeman-Tukey
(Hellinger-distance) statistic, the Kolmogorov-Smirnov
statistic, the Cramer-von Mises statistic as described in
Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>,
and the root-mean-square statistic, see Perkins,
Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.
knitr, dgof, cvmdisc, bench, testthat (>= 3.0.0), rmarkdown

Implements fast Monte Carlo simulations for
goodness-of-fit (GOF) tests for discrete distributions. This
includes tests based on the Chi-squared statistic, the
log-likelihood-ratio (G^2) statistic, the Freeman-Tukey
(Hellinger-distance) statistic, the Kolmogorov-Smirnov
statistic, the Cramer-von Mises statistic as described in
Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828>,
and the root-mean-square statistic, see Perkins,
Tygert, and Ward (2011) <doi:10.1016/j.amc.2011.03.124>.