Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.

Select sampling methods for probability samples using large data sets.  This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).

Package provides the possibility to sampling complete datasets 
  from a normal distribution to simulate cluster randomized trails for different study designs.

A robust solution employing the SRS (Simple Random Sampling),
    systematic and PPS (Probability Proportional to Size) sampling
    methods, ensuring a methodical and representative selection of data.
    Seamlessly allocate predetermined allocations to smaller levels.

Functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling.

Tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the 'genalg' package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" <doi:10.48550/arXiv.2004.09366>.