Shows statistics about bytes contained in a file 
  as a circle graph of deviations from mean in sigma increments. 
  The function can be useful for statistically analyze the content of files 
  in a glimpse: text files are shown as a green centered crown, compressed 
  and encrypted files should be shown as equally distributed variations with 
  a very low CV (sigma/mean), and other types of files can be classified between 
  these two categories depending on their text vs binary content, which can be 
  useful to quickly determine how information is stored inside them (databases, 
  multimedia files, etc).

Provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.

The c060 package provides additional functions to perform stability selection, model validation and parameter tuning for glmnet models.

Dataset containing cumulative COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.

Provides a self-contained set of methods to aid clinical trial
    safety investigators, statisticians and researchers, in the early
    detection of adverse events using groupings 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.

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).