Performs a joint analysis of experiments with mixtures and random effects, taking on a process variable represented by a covariable.

Computation of key characteristics and plots for blinded sample size recalculation.
   Continuous as well as binary endpoints are supported in superiority and non-inferiority trials.
   See Baumann, Pilz, Kieser (2022) <doi:10.32614/RJ-2022-001> for a detailed description.
   The implemented methods include the approaches by
   Lu, K. (2019) <doi:10.1002/pst.1737>,
   Kieser, M. and Friede, T. (2000) <doi:10.1002/(SICI)1097-0258(20000415)19:7%3C901::AID-SIM405%3E3.0.CO;2-L>,
   Friede, T. and Kieser, M. (2004) <doi:10.1002/pst.140>, 
   Friede, T., Mitchell, C., Mueller-Veltern, G. (2007) <doi:10.1002/bimj.200610373>, and
   Friede, T. and Kieser, M. (2011) <doi:10.3414/ME09-01-0063>.

Randomly reassigns the group identifications to one of the variables of the 
   database, say Treatment, and randomly reassigns the observation numbers of the dataset. 
   Reorders the observations according to these new numbers. Centers each group of Treatment
   at the grand mean in order to further mask the treatment. An unmasking function is 
   provided so that the user can identify the potential outliers in terms of their original 
   values when blinding is no longer needed. It is suggested that a forward search procedure 
   be performed on the masked data. Details of some forward search functions may be found in
   <https://CRAN.R-project.org/package=forsearch>.

A blind spike program provides samples to a laboratory in order to  perform quality control (QC) checks. The samples provided are of a known quantity to the tester. The laboratory is typically uninformed of that the sample provided is a QC sample.

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.