Suggests: Bchron, countrycode, dataverse, ggplot2, ggridges, knitr,
lwgeom, mapview, openxlsx, plyr, rgeos, rmarkdown,
rnaturalearth, rworldmap, rworldxtra, sf, stringdist, testthat

graphics, httr (>= 1.4.1), magrittr (>= 1.5), pbapply (>=
Suggests: Bchron, countrycode, dataverse, ggplot2, ggridges, globe,
knitr, lwgeom, mapview, openxlsx, plyr, rgeos, rmarkdown,
Packaged: 2020-01-12 16:10:20 UTC; schmid@shh.mpg.de

Title: Methods for Detecting Safety Signals in Clinical Trials Using
Description: Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) (EPSRC) funded project which was named CASE 2/12.

Description: Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class.The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

Description: Methods for detecting safety signals in clinical trials using groupings of adverse events by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

Title: Compare Two Classifications or Clustering Solutions of Varying
Description: Compare two classifications or clustering solutions that may or may
not have the same number of classes, and that might have hard or soft
(fuzzy, probabilistic) membership. Calculate various metrics to assess how
the clusters compare to each other. The calculations are simple, but provide
a handy tool for users unfamiliar with matrix multiplication. This package
is not geared towards traditional accuracy assessment for classification/
mapping applications - the motivating use case is for comparing a
probabilistic clustering solution to a set of reference or existing class
labels that could have any number of classes (that is, without having to
degrade the probabilistic clustering to hard classes).