statistics and probability courses, but with powerful support more advanced
probability calculations. This includes the creation of joint random
variables, and the derivation and manipulation of their conditional and

Title: Coordinate-Descent Algorithm for Discrete Data
Authors@R: person("Jiaying", "Gu", email = "gujy.lola@gmail.com", role = c("aut", "cre"))
Description: 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>.
Packaged: 2016-08-11 03:26:41 UTC; jiayinggu

Packaged: 2016-11-23 05:04:10 UTC; jiayinggu

Imports: Rcpp (>= 0.11.0), sparsebnUtils (>= 0.0.4), igraph
Packaged: 2017-03-10 06:32:30 UTC; jiayinggu

Packaged: 2017-04-28 05:43:25 UTC; jiayinggu

Title: Coordinate-Descent Algorithm for Learning Sparse Discrete
The algorithm is described in Gu, Fu and Zhou (2016) <arXiv:1403.2310>.
Packaged: 2017-09-10 13:42:40 UTC; jiayinggu

Packaged: 2019-12-21 21:31:09 UTC; jiayinggu

Packaged: 2020-03-12 05:56:10 UTC; jiayinggu

Title: Simulated Goodness-of-Fit Tests for Discrete Distributions
Description: 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
Suggests: knitr, dgof, RVAideMemoire, bench, testthat (>= 3.0.0),

Description: Implements Monte Carlo simulations for
includes tests based on the Chi-squared statistic, the
log-likelihood-ratio (G^2) statistic, the Freeman-Tukey
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,
Suggests: knitr, dgof, cvmdisc, bench, testthat (>= 3.0.0), rmarkdown

Description: Implements fast Monte Carlo simulations for

Title: Data Preprocessing, Discretization for Classification
Description: A collection of supervised discretization
algorithms. It can also be grouped in terms of top-down or
bottom-up, implementing the discretization algorithms.