utils, MASS, nlme, parallel, stats, Rcpp (>= 0.12.14),
stringr, plyr, abind, methods, RcppParallel

knitr, RMark, rgdal, R2ucare, rmarkdown, testthat

utils, MASS, nlme, parallel, stats, Rcpp (>= 0.12.14),
stringr, plyr, abind, methods, RcppParallel (>= 5.1.1)

Non-spatial and spatial open-population capture-recapture analysis.
abind, MASS, methods, nlme, parallel, plyr, Rcpp (>= 0.12.14),
RcppParallel (>= 5.1.1), stats, stringr, utils

knitr, RMark, rmarkdown, testthat, R2ucare

EBGM Scores for Mining Large Contingency Tables
c(
person("John", "Ihrie", email = "John.Ihrie@fda.hhs.gov",
role = c("cre", "aut")),
person("Travis", "Canida", email = "Travis.Canida@fda.hhs.gov", role = "aut"),
person("Ismaïl", "Ahmed", role = "ctb",
comment = "author of 'PhViD' package (derived code)"),
person("Antoine", "Poncet", role = "ctb",
comment = "author of 'PhViD' package (derived code)"))
An implementation of DuMouchel's (1999)
<doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the
market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and
quantile scores from the posterior distribution using the Gamma-Poisson
Shrinker (GPS) model to find unusually large cell counts in large, sparse
contingency tables. Can be used to find unusually high reporting rates of
adverse events associated with products. In general, can be used to mine any
database where the co-occurrence of two variables or items is of interest.
Also calculates relative and proportional reporting ratios. Builds on the work
of the 'PhViD' package, from which much of the code is derived. Some of the
added features include stratification to adjust for confounding variables and
data squashing to improve computational efficiency.
John Ihrie [cre, aut],
Travis Canida [aut],
Ismaïl Ahmed [ctb] (author of 'PhViD' package (derived code)),
Antoine Poncet [ctb] (author of 'PhViD' package (derived code))

c(
person("John", "Ihrie", email = "John.Ihrie@fda.hhs.gov",
role = c("cre", "aut")),
person("Travis", "Canida", email = "Travis.Canida@fda.hhs.gov", role = "aut"),
person("Ismaïl", "Ahmed", role = "ctb",
comment = "author of 'PhViD' package (derived code)"),
person("Antoine", "Poncet", role = "ctb",
comment = "author of 'PhViD' package"),
person("Sergio", "Venturini", role = "ctb",
comment = "author of 'mederrRank' package (derived code)"))
An implementation of DuMouchel's (1999)
<doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the
market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and
quantile scores from the posterior distribution using the Gamma-Poisson
Shrinker (GPS) model to find unusually large cell counts in large, sparse
contingency tables. Can be used to find unusually high reporting rates of
adverse events associated with products. In general, can be used to mine any
database where the co-occurrence of two variables or items is of interest.
Also calculates relative and proportional reporting ratios. Builds on the work
of the 'PhViD' package, from which much of the code is derived. Some of the
added features include stratification to adjust for confounding variables and
data squashing to improve computational efficiency. Now includes an
implementation of the EM algorithm for hyperparameter estimation loosely
derived from the 'mederrRank' package.
John Ihrie [cre, aut],
Travis Canida [aut],
Ismaïl Ahmed [ctb] (author of 'PhViD' package (derived code)),
Antoine Poncet [ctb] (author of 'PhViD' package),
Sergio Venturini [ctb] (author of 'mederrRank' package (derived code))