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.

Non-Uniform Fast Fourier Transform ('NUFFT')-accelerated local polynomial
    regression and kernel density estimation for large, scattered, or
    complex-valued datasets. Provides automatic bandwidth selection via
    Generalized Cross-Validation (GCV) for regression and Likelihood
    Cross-Validation (LCV) for density estimation. This is the 'R' port of the
    'fastLPR' 'MATLAB'/'Python' toolbox, achieving O(N + M log M) computational
    complexity through custom 'NUFFT' implementation with Gaussian gridding.
    Supports 1D/2D/3D data, complex-valued responses, heteroscedastic variance
    estimation, and confidence interval computation. Performance optimized with
    vectorized 'R' code and compiled helpers via 'Rcpp'/'RcppArmadillo'.
    Extends the 'FKreg' toolbox of Wang et al. (2022)
    <doi:10.48550/arXiv.2204.07716> with 'Python' and 'R' ports. Applied in
    Li et al. (2022) <doi:10.1016/j.neuroimage.2022.119190>. Uses 'NUFFT'
    methods based on Greengard and Lee (2004) <doi:10.1137/S003614450343200X>,
    binning-accelerated kernel estimation of Wand (1994)
    <doi:10.1080/10618600.1994.10474656>, and local polynomial regression
    framework of Fan and Gijbels (1996, ISBN:978-0412983214).

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>.