Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data
    and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two 
    datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, 
    tools for preparing, adjusting, and summarizing data merges are included. The package implements methods 
    described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of 
    Large-scale Administrative Records'' <doi:10.1017/S0003055418000783> and is available 
    at <https://imai.fas.harvard.edu/research/linkage.html>.

Computes various Local Indicators of Spatial Association (LISA)
    statistics, including univariate and bivariate local Moran's I,
    Empirical Bayes local Moran's I, univariate and multivariate local
    Geary's C, and Getis-Ord G and G* statistics. The methods follow
    Anselin (1995), Getis and Ord (1992), and Anselin (2019). Leverages a
    high-performance, plain-C backend with optional 'OpenMP' multi-core
    support for fast permutation-based pseudo-p-value calculation. Accepts any
    'spdep' listw spatial weight matrix, including custom and
    non-contiguity weights. Uses sample standardisation (n-1) and
    'rgeoda'-style permutation p-values. Output cluster codes match 'rgeoda'
    conventions, including the Isolated category for observations without
    neighbours.

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and 
    information criteria for a grid of the regularization parameter. 
    The computations are based on the 'C++' library 'Armadillo' through the 'R' package 'Rcpp'.

Provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the 'RcppNumerical' package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.