https://rjauslinbfs.r-universe.devRecent package updates in rjauslinbfsR-universehttps://github.com/rjauslinbfs.pnghttps://rjauslinbfs.r-universe.devFri, 31 Jan 2025 12:49:45 GMTraphael.jauslin@bfs.admin.ch (Raphaël Jauslin)Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.https://github.com/r-universe/rjauslin/actions/runs/37449847079Fri, 31 Jan 2025 12:49:45 GMTWaveSampling0.1.4successhttps://rjauslin.r-universe.devhttps://github.com/rjauslin/wavesamplingraphael.jauslin@bfs.admin.ch (Raphael Jauslin)Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) <doi:10.1007/s42081-021-00134-y>. The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé <doi:10.1016/j.jspi.2022.12.003>.https://github.com/r-universe/rjauslin/actions/runs/37905930349Fri, 31 Jan 2025 12:36:42 GMTStratifiedSampling0.4.2successhttps://rjauslin.r-universe.devhttps://github.com/rjauslin/stratifiedsamplingot_matching.Rmdot_matching.htmlStatistical Matching using Optimal Transport2021-05-17 09:16:082025-01-31 10:57:23