Functions and datasets to support Valliant, Dever, and Kreuter (2018), <doi:10.1007/978-3-319-93632-1>, "Practical Tools for Designing and Weighting Survey Samples". Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.

The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.

Build friendly R packages that
    praise their users if they have done something
    good, or they just need it to feel better.

A toolbox of fast, native and parallel implementations of various information-based importance criteria estimators and feature selection filters based on them, inspired by the overview by Brown, Pocock, Zhao and Lujan (2012) <https://www.jmlr.org/papers/v13/brown12a.html>.
 Contains, among other, minimum redundancy maximal relevancy ('mRMR') method by Peng, Long and Ding (2005) <doi:10.1109/TPAMI.2005.159>; joint mutual information ('JMI') method by Yang and Moody (1999) <https://papers.nips.cc/paper/1779-data-visualization-and-feature-selection-new-algorithms-for-nongaussian-data>; double input symmetrical relevance ('DISR') method by Meyer and Bontempi (2006) <doi:10.1007/11732242_9> as well as joint mutual information maximisation ('JMIM') method by Bennasar, Hicks and Setchi (2015) <doi:10.1016/j.eswa.2015.07.007>.

A testing workbench to evaluate tools that calculate precision-recall curves.
    Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.