The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2021) <arXiv:2105.04451>.
Cargo (>= 1.49.0) for installation from sources:
see file INSTALL

Cargo (>= 1.51.0) for installation from sources:
see INSTALL file

c(person(given="David B.",family="Dahl",role=c("aut","cre"),email="dahl@stat.byu.edu"), person(given="Devin J.",family="Johnson",role=c("aut"),email="devin.j.johnson7@gmail.com"), person(given="Peter",family="Müller",role=c("aut"),email="pmueller@math.utexas.edu"))
David B. Dahl [aut, cre],
Devin J. Johnson [aut],
Peter Müller [aut]

Cargo (>= 1.40.0) for installation from sources:
see file INSTALL

c(
person("Matthew Lincoln", email = "matthew.d.lincoln@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-4387-3384")))
Take real or simulated data and salt it with errors commonly
found in the wild, such as pseudo-OCR errors, Unicode problems, numeric
fields with nonsensical punctuation, bad dates, etc.
charlatan, testthat (>= 2.0.0), tibble, covr
Matthew Lincoln [aut, cre] (<https://orcid.org/0000-0002-4387-3384>)

A Collection of Techniques Correcting for Sample Selection Bias
Norbert Krautenbacher, Kevin Strauss, Maximilian Mandl, Christiane Fuchs
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
stats, mvtnorm, dplyr, smotefamily, e1071, ranger, pROC, FNN