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.

Implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators. The algorithm is described in Duembgen, Nordhausen and Schuhmacher (2016) <doi:10.1016/j.jmva.2015.11.009>.

Fast implementation of data structures, including a key-value
    store, stack, and queue. Environments are commonly used as key-value stores
    in R, but every time a new key is used, it is added to R's global symbol
    table, causing a small amount of memory leakage. This can be problematic in
    cases where many different keys are used. Fastmap avoids this memory leak
    issue by implementing the map using data structures in C++.

Package providing a fast match() replacement for cases
	that require repeated look-ups. It is slightly faster that R's
	built-in match() function on first match against a table, but
	extremely fast on any subsequent lookup as it keeps the hash
	table in memory.