Matching Multiply Imputed Datasets (MatchThem)
Provides the necessary tools for the pre-processing technique of matching in multiply imputed datasets, to improve the robustness and transparency of deriving causal inferences from studying these datasets. This package includes functions to perform propensity score matching within or across the imputed datasets as well as to estimate weights (including inverse propensity score weights) of observations, to analyze each matched or weighted datasets using parametric or non-parametric statistical models, and to combine the obtained results from these models according to Rubin’s rules. Please see the package repository <https://…/MatchThem> for details.

Recursive Partitioning Based Multivariate Adaptive Regression Models, Classification Trees, Survival Trees (macs)
Implements recursive partitioning based, nonparametric methods for high dimensional regression and classification. Depending on the aims of data analysis as well as the structures of the data, macs provides three major functions: multivariate adaptive regression models, classification trees and survival trees. A list of references for this package is, Zhang, H. (1997) <doi:10.1080/10618600.1997.10474728>, Zhang, H. et al. (1999) <ISBN:978-1-4757-3027-2>, Zhang, H. et al. (2014) <doi:10.1002/gepi.21843>.

Logistic Regression Trees (glmtree)
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user’s criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://…/>.

Forecast Verification for Extreme Events (extremeIndex)
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.

A Collection of Useful Functions by John (usefun)
A set of general functions that I have used in various projects and in other R packages. They support some miscellaneous operations on data frames, matrices and vectors: adding a row on a ternary (3-value) data.frame based on positive and negative vector-indicators, rearranging a list of data.frames by rownames, pruning rows or columns of a data.frame that contain only one specific value given by the user, checking for matrix equality, pruning and reordering a vector according to the common elements between its names and elements of another given vector, finding the non-common elements between two vectors (outer-section), normalization of a vector, matrix or data.frame’s numeric values in a specified range, pretty printing of vector names and values in an R notebook (common names and values between two vectors also supported), retrieving the parent directory of any string path, checking whether a numeric value is inside a given interval, trim the decimal points of a given numeric value, quick saving of data to a file, making a multiple densities plot and a color bar plot and executing a plot string expression while generating the result to the specified file format.