Performs Bayesian calibration of computer models as per
 Kennedy and O'Hagan 2001.  The package includes routines to find the
 hyperparameters and parameters; see the help page for stage1() for a
 worked example using the toy dataset.  A tutorial is provided in the
 calex.Rnw vignette; and a suite of especially simple one dimensional
 examples appears in inst/doc/one.dim/.

Transforms your uncalibrated Machine Learning scores to well-calibrated prediction estimates that can be interpreted as probability estimates. The implemented BBQ (Bayes Binning in Quantiles) model is taken from Naeini (2015, ISBN:0-262-51129-0). Please cite this paper: Schwarz J and Heider D, Bioinformatics 2019, 35(14):2458-2465.

Assesses the quality of estimates made by complex sample designs,
  following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, <https://www.ine.cl/docs/default-source/institucionalidad/buenas-pr%C3%A1cticas/clasificaciones-y-estandares/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-publicaci%C3%B3n-27022020.pdf>), (Economics Survey Standard 2024, <https://www.ine.gob.cl/docs/default-source/buenas-practicas/directrices-metodologicas/estandares/documentos/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-econ%C3%B3micas.pdf?sfvrsn=201fbeb9_2>)
  and by Economic Commission  for Latin America and 
  Caribbean (2020, <https://repositorio.cepal.org/bitstream/handle/11362/45681/1/S2000293_es.pdf>), (2024, <https://repositorio.cepal.org/server/api/core/bitstreams/f04569e6-4f38-42e7-a32b-e0b298e0ab9c/content>).

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.

Compile inline 'C' code and easily call with
 automatically generated wrapper functions.  By allowing user-defined headers
 and compilation flags (preprocessor, compiler and linking flags) the user 
 can configure optimization options and linking to third party 
 libraries. Multiple functions may be defined in a single block of code - which 
 may be defined in a string or a path to a source file.