Provides a minimal interface for applying
    annotators from the 'Stanford CoreNLP' java library. Methods
    are provided for tasks such as tokenisation, part of speech
    tagging, lemmatisation, named entity recognition, coreference
    detection and sentiment analysis.

Core functions for simulating quantities of interest
    from generalised linear models (GLM). This package will form the backbone of
    a series of other packages that improve the interpretation of GLM estimates.

Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.

Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features 
	using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables 
	using data transformations.

A matrix of agreement patterns and counts for record pairs is the input for the procedure.  An EM algorithm is used to impute plausible values for missing record pairs.  A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual.

In metabolic flux experiments tracer molecules (often glucose
    containing labelled carbon) are incorporated in compounds measured
    using mass spectrometry. The mass isotopologue distributions of these
    compounds needs to be corrected for natural abundance of labelled
    carbon and other effects, which are specific on the compound and
    ionization technique applied. This package provides functions to
    correct such effects in gas chromatography atmospheric pressure
    chemical ionization mass spectrometry analyses.

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.