Nearest Centroid (NC) Sampling (NCSampling)
Provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.

SQL Server R Database Interface (DBI) and ‘dplyr’ SQL Backend (RSQLServer)
Utilises The ‘jTDS’ project’s ‘JDBC’ 3.0 ‘SQL Server’ driver to extend ‘DBI’ classes and methods. The package also implements a ‘SQL’ backend to the ‘dplyr’ package.

A Way of Writing Functions that Quote their Arguments (quotedargs)
A facility for writing functions that quote their arguments, may sometimes evaluate them in the environment where they were quoted, and may pass them as quoted to other functions.

Easily Build and Evaluate Machine Learning Models (easyml)
Easily build and evaluate machine learning models on a dataset. Machine learning models supported include penalized linear models, penalized linear models with interactions, random forest, support vector machines, neural networks, and deep neural networks.

Sequential Invariant Causal Prediction (seqICP)
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is ‘seqICP’, which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method ‘seqICPnl’, which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines ‘seqICP.s’ and ‘seqICPnl.s’ corresponding to the respective main methods.