Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.

Data sets used in the book Marra and Radice (2025, ISBN:9781032973111) "Copula Additive Distributional Regression Using R", for illustrating the fitting of various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association.

Functions for the g-and-k and generalised g-and-h distributions.

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

A simple way to interact with and extract data from
  the official 'Google Knowledge Graph' API <https://developers.google.com/knowledge-graph/>.

Imports the 'gkmSVM' v2.0 functionalities into R <https://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
    Users should note that the suggested packages 'rtracklayer', 'GenomicRanges', 'BSgenome', 'BiocGenerics', 
    'Biostrings', 'GenomeInfoDb', 'IRanges', and 'S4Vectors' are all BioConductor packages <https://bioconductor.org>.

Kernel regularized least squares, also known as kernel ridge regression, 
    is a flexible machine learning method. This package implements this method by 
    providing a smooth term for use with 'mgcv' and uses random sketching to 
    facilitate scalable estimation on large datasets. It provides additional 
    functions for calculating marginal effects after estimation and for use with 
    ensembles ('SuperLearning'), double/debiased machine learning ('DoubleML'), 
    and robust/clustered standard errors ('sandwich'). Chang and Goplerud (2024)
    <doi:10.1017/pan.2023.27> provide further details.

Implements the Gaussian Kernel Robust Regression (GKRReg / GKRR)
    method proposed by De Carvalho, Lima Neto and Ferreira (2017)
    <doi:10.1016/j.neucom.2016.12.035>. The method re-weights observations
    iteratively using the Gaussian kernel so that poorly-fitted observations
    (outliers, leverage points) receive small weights, yielding resistance to
    Y-space outliers, X-space outliers and leverage points. Convergence is
    guaranteed by Propositions 4.1 and 4.2 of the original paper. Three
    estimators for the kernel width hyper-parameter are provided (S1: Caputo,
    S2: pairwise median, S3: residual variance). Inference is provided via an
    analytic sandwich variance estimator (default) or via bootstrap
    (percentile, normal and BCa intervals with p-values) through gkrr_boot().
    Six real datasets from the robust regression literature are included to
    facilitate reproducible comparisons.