Convert Categorical Representations of Logicals to Actual
Logicals
Oliver Keyes [aut, cre], Ruben C. Arslan [ctb], Christopher Akiki [ctb], Mine Cetinkaya-Rundel [ctb], Peter Meissner [ctb],
Ilaria Prosdocimi [ctb], Thomas Leeper [ctb], Amy Lee [ctb], Adolfo Álvarez [ctb]
Survey systems and other third-party data sources commonly use non-standard representations of logical values when
it comes to qualitative data - "Yes", "No" and "N/A", say. batman is a package designed to seamlessly convert these into logicals.
It is highly localised, and contains equivalents to boolean values in languages including German, French, Spanish, Italian,
Turkish, Chinese and Polish.

Martyn Plummer <plummer@iarc.fr>,
Paul Gilbert <pgilbert@bank-banque-canada.ca>

An R Package for Data Reduction by Battery Reduction
c( person("Chunqiao", "Luo", email = "chunqiaoluo@gmail.com", role = c("aut", "cre")),person("Ralph", "D'Agostino ", role = c("aut"), comment = "This package is derived from Battery Reduction Macro at http://www.lexjansen.com/nesug/nesug92/NESUG92090.pdf"))
Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.
Chunqiao Luo [aut, cre],
Ralph D'Agostino [aut] (This package is derived from Battery Reduction
Macro at http://www.lexjansen.com/nesug/nesug92/NESUG92090.pdf)

Bayesian Multidimensional Scaling and Choice of Dimension
c(person(given = "Man-Suk",
family = "Oh",
role = c("aut", "cre"),
email = "msoh@ewha.ac.kr"),
person(given = "Eun-Kyung",
family = "Lee",
role = c("aut")))
Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001)  <doi:10.1198/016214501753208690>.
Rcpp (>= 1.0.7), progress, ggplot2, shinythemes, shiny,
gridExtra, rgl, ggpubr
Man-Suk Oh [aut, cre],
Eun-Kyung Lee [aut]

Rcpp (>= 1.0.7), progress, ggplot2, shinythemes, shiny, ggpubr

Evan A Martin [aut, cre], Audrey Qiuyan Fu [aut]
A Bayesian hybrid approach for inferring Directed Acyclic Graphs
(DAGs) for continuous, discrete, and mixed data. The algorithm can use the
graph inferred by another more efficient graph inference method as input;
the input graph may contain false edges or undirected edges but can help
reduce the search space to a more manageable size. A Bayesian Markov chain
Monte Carlo algorithm is then used to infer the probability of direction and
absence for the edges in the network.
References:
Martin and Fu (2019) <arXiv:1909.10678>.