Fitting a fast Laplace approximation for Spatial Generalized Linear Mixed Model as
  described in Park and Lee (2021) <https://github.com/sangwan93/fastLaplace/blob/main/FastLaplaceMain.pdf>.

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>.

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.

Calculates marginal effects based on logistic model objects such as 'glm' or 'speedglm' at the average (default) or at given values using finite differences. It also returns confidence intervals for said marginal effects and the p-values, which can easily be used as input in stargazer. The function only returns the essentials and is therefore much faster but not as detailed as other functions available to calculate marginal effects. As a result, it is highly suitable for large datasets for which other packages may require too much time or calculating power.