R6, ggplot2, DiceKriging, DiceDesign, MASS, coda, parallel,
gridExtra, gtools
'functions.R' 'models.R' 'forecast.R' 'prior.R'
'RcppExports.R' 'CaliCo-package.R' 'Kernel.R' 'calibration.R'
'sequentialDesign.R'

Mapping ML Scores to Calibrated Predictions
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).
ggplot2, pROC, reshape2, parallel, foreach, utils, stats,
fitdistrplus, doParallel

ggplot2, pROC, reshape2, parallel, foreach, stats,
fitdistrplus, doParallel

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.

Weighting and Estimation for Panel Data with Non-Response
Function to calculate weights and estimates for panel data with non-response.

Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response.

Functions to calculate weights, estimates of changes and corresponding variance estimates for panel data with non-response. Partially overlapping samples are handled. Initially, weights are calculated by linear calibration. By default, the survey package is used for this purpose. It is also possible to use ReGenesees, which can be installed from <https://github.com/DiegoZardetto/ReGenesees>. Variances of linear combinations (changes and averages) and ratios are calculated from a covariance matrix based on residuals according to the calibration model. The methodology was presented at the conference, The Use of R in Official Statistics, and is described in Langsrud (2016) <http://www.revistadestatistica.ro/wp-content/uploads/2016/06/RRS2_2016_A021.pdf>.