Perform mediation analysis via a fast-and-robust bootstrap test, as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022) <doi:10.18637/jss.v103.i13>.

Robust Generalized Linear Models (GLM) using Mixtures
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
fastGHQuad, stats, bbmle, MASS, VGAM, actuar, Rcpp (>=
0.12.15), methods, boot, numDeriv

fastGHQuad, stats, bbmle, MASS, VGAM, actuar, Rcpp (>=
0.12.15), methods, boot, numDeriv, parallel, doParallel,
foreach, doRNG

fastGHQuad, stats, bbmle, VGAM, actuar, Rcpp (>= 0.12.15),
methods, boot, numDeriv, parallel, doParallel, foreach, doRNG,
MASS

Robust Nonparametric Two-Sample Tests for Location/Scale
c(person(given = "Sermad", family = "Abbas",
email = "abbas@statistik.tu-dortmund.de", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-9162-9792")),
person(given = "Barbara", family = "Brune", email = "bbrune@statistik.tu-dortmund.de", role = "aut",
comment = c(ORCID = "0000-0002-3154-8445")),
person(given = "Roland", family = "Fried", email = "fried@statistik.tu-dortmund.de", role = "aut"))
Sermad Abbas [aut, cre] (<https://orcid.org/0000-0001-9162-9792>),
Barbara Brune [aut] (<https://orcid.org/0000-0002-3154-8445>),
Roland Fried [aut]
Implementations of several robust nonparametric two-sample tests
for location or scale differences. The test statistics are based on robust
location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators
as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>.
The p-values can be computed via the permutation principle, the randomization principle, or by using
the asymptotic distributions of the test statistics under the null hypothesis, which ensures
(approximate) distribution independence of the test decision. To test for a difference in
scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>.
Random noise on a small range can be added to the original observations in order to
hold the significance level on data from discrete distributions.
The location tests assume homoscedasticity and the scale tests require the
location parameters to be zero.
Rdpack, gtools, robustbase, statmod, stats, utils, checkmate
testthat, knitr, rmarkdown, usethis, covr