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.

In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package. M.Ballin, G.Barcaroli (2020) <arXiv:2004.09366> "R package SamplingStrata: new developments and extension to Spatial Sampling".