Imports: randomForest, ggplot2, data.table, stats, rlist, methods
Suggests: foreach, knitr, rmarkdown, correlbinom
Packaged: 2020-02-19 19:28:53 UTC; samirrachidzaim-admin

Imports: randomForest, data.table, stats, rlist
Packaged: 2020-03-26 16:11:02 UTC; samirrachidzaim-admin

Title: Generates binomial random numbers via the coupling from the past
Description: Binomial random numbers are generated via the perfect
sampling algorithm. At each iteration dual markov chains are
generated and coalescence is checked. In case coalescence
occurs, the resulting number is outputted. In case not, then
the algorithm is restarted from T(t)=2*T(t) until coalescence

Title: Efficient MCMC for Binomial Logit Models
Description: The R package contains different MCMC schemes to estimate
the regression coefficients of a binomial (or binary) logit
model within a Bayesian framework: a data-augmented
independence MH-sampler, an auxiliary mixture sampler and a
hybrid auxiliary mixture (HAM) sampler. All sampling procedures
are based on algorithms using data augmentation, where the
regression coefficients are estimated by rewriting the logit
model as a latent variable model called difference random

Description: The R package contains different MCMC schemes to estimate the regression coefficients of a binomial (or binary) logit model within a Bayesian framework: a data-augmented independence MH-sampler, an auxiliary mixture sampler and a hybrid auxiliary mixture (HAM) sampler. All sampling procedures are based on algorithms using data augmentation, where the regression coefficients are estimated by rewriting the logit model as a latent variable model called difference random utility model (dRUM).

person("Ursula", "Laa", email = "ursula.laa@monash.edu", role = "cre"),
person("Heike", "Hofmann", email = "hofmann@iastate.edu", role = "aut"),
person("Lee", "Wilkinson", email = "leland@spss.com", role = "aut"),
person("Hadley", "Wickham", email = "h.wickham@gmail.com", role = "aut"),
person("Duncan", "Temple Lang", role = "aut"),
person("Anushka", "Anand", role = "aut")
Description: Calculates graph theoretic scagnostics.  Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot.  They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.