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.

An implementation of Bayesian online changepoint detection 
  (Adams and MacKay (2007) <doi:10.48550/arXiv.0710.3742>) with an option for probability 
  based outlier detection and removal (Wendelberger et. al. (2021) <doi:10.48550/arXiv.2112.12899>). 
  Building on the independent multivariate constant mean model implemented in 
  the 'R' package 'ocp', this package models multivariate data as multivariate 
  normal about a linear trend, defined by user input covariates, with an 
  unstructured error covariance. Changepoints are identified based on a 
  probability threshold for windows of points.

Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). The 'robomit' package computes i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). The code is based on the 'psacalc' command in 'Stata'. Additionally, 'robomit' offers a set of sensitivity analysis and visualization functions. See Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>. Additionally, see Diegert, P., Masten, M. A., & Poirier, A. (2022) for a recent discussion of the topic: <doi:10.48550/arXiv.2206.02303>.