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

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

Suggests: ReGenesees, testthat (>= 2.1.0)
Description: 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>.

Authors@R: c(person("De Cock", "Bavo", role = c("aut", "cre"),
person("Nieboer", "Daan", role = "aut"),
person("Van Calster", "Ben", role = "aut"),
Description: Plots calibration curves and computes statistics for assessing calibration performance. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.
Imports: grDevices, graphics, methods, stats, utils, survival, Hmisc
Maintainer: De Cock Bavo <bavo.decock@kuleuven.be>

Title: Providing an Interface to the e-Shotgun Algorithm for Bayesian
Authors@R: c(person("Lennard", "Reese", role = c("aut", "cre"), email = "CeSMaintainer@web.de"),
person("Frederik", "Rehbach", role = "ctb"))
Description: A set of tools for the usage of the e-shotgun algorithm for Bayesian optimization.
The e-shotgun was originally developed by "George De Ath, Richard M. Everson, Jonathan E. Fieldsend, and Alma A. M. Rahat. 2020.
e-shotgun : e-greedy Batch Bayesian Optimisation. In Genetic and Evolutionary Computation Conference (GECCO ’20), July 8–12, 2020, Cancún, Mexico.