Implements multiple allocation and selection strategies of 
    sampling to construct core collections primarily from clustered or grouped 
    germplasm collection data. Provides methods for allocating entries to 
    clusters/groups based on group sizes, group-wise distance-based diversity 
    metrics, and group-wise diversity index estimates. Includes procedures for 
    selecting entries within clusters/groups through random sampling, genetic 
    distance-based approaches, and optimized diversity metric–based selection 
    methods. See the package documentation for more, including full list of 
    references for the methods implemented.

Provides a collection of sample datasets 
    on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data.
    It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

Two-step
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2),
   endogenous switching regression models (Tobit-5),
   sample selection models with binary dependent outcome variable,
   interval regression with sample selection (only ML estimation),
   and endogenous treatment effects models.
   These methods are described in the three vignettes
   that are included in this package 
   and in econometric textbooks such as
   Greene (2011, Econometric Analysis, 7th edition, Pearson).