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.

A unified 'Formula' interface to the Synthetic Control Method
    (SCM) and related panel-data causal inference estimators: Synthetic
    Difference-in-Differences (SDID), Generalized Synthetic Control (GSC),
    Matrix Completion (MC), Time-Aware Synthetic Control (TASC), and Synthetic
    Interventions (SI), together with an experimental-design variant.
    Computational bottlenecks (quadratic programming, singular value
    decomposition, and Kalman filtering) are implemented in 'C++' via
    'RcppArmadillo'. Methods are described in Abadie, Diamond and Hainmueller
    (2010) <doi:10.1198/jasa.2009.ap08746>, Arkhangelsky, Athey, Hirshberg,
    Imbens and Wager (2021) <doi:10.1257/aer.20190159>, Xu (2017)
    <doi:10.1017/pan.2016.2>, Athey, Bayati, Doudchenko, Imbens and Khosravi
    (2021) <doi:10.1080/01621459.2021.1891924>, and Agarwal, Shah and Shen
    (2025) <doi:10.1287/opre.2025.1590>.

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.