R Interface to NLopt

Solve optimization problems using an R interface to NLopt. NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. See < https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more information on the available algorithms. Building from included sources requires 'CMake'. On Linux and 'macOS', if a suitable system build of NLopt (2.7.0 or later) is found, it is used; otherwise, it is built from included sources via 'CMake'. On Windows, NLopt is obtained through 'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for 'R >= 4.2.0'.

Covariate Balancing Propensity Score

Implements the covariate balancing propensity score (CBPS) proposed by Imai and Ratkovic (2014) . The propensity score is estimated such that it maximizes the resulting covariate balance as well as the prediction of treatment assignment. The method, therefore, avoids an iteration between model fitting and balance checking. The package also implements optimal CBPS from Fan et al. (in-press) , several extensions of the CBPS beyond the cross-sectional, binary treatment setting. They include the CBPS for longitudinal settings so that it can be used in conjunction with marginal structural models from Imai and Ratkovic (2015) , treatments with three- and four-valued treatment variables, continuous-valued treatments from Fong, Hazlett, and Imai (2018) , propensity score estimation with a large number of covariates from Ning, Peng, and Imai (2020) , and the situation with multiple distinct binary treatments administered simultaneously. In the future it will be extended to other settings including the generalization of experimental and instrumental variable estimates.

Ensemble Taxonomic Assignments of Amplicon Sequencing Data

Creates ensemble taxonomic assignments of amplicon sequencing data in R using outputs of multiple taxonomic assignment algorithms and/or reference databases. Includes flexible algorithms for mapping taxonomic nomenclatures onto one another and for computing ensemble taxonomic assignments.

Make Labeling of R Data Sets Easy

Assign meaningful labels to data frame columns. 'labelmachine' manages your label assignment rules in 'yaml' files and makes it easy to use the same labels in multiple projects.

Group Assignment Tool

An efficient algorithm to generate group assignments for classroom settings while minimizing repeated pairings across multiple rounds.

Unpacking Assignment for Lists via Pattern Matching

Provides an operator for assigning nested components of a list to names via a concise pattern matching syntax. This is especially convenient for assigning individual names to the multiple values that a function may return in the form of a list, and for extracting deeply nested list components.

Recording Synchronisation, Call Detection and Assignment, Audio Analysis

Intended to analyse recordings from multiple microphones (e.g., backpack microphones in captive setting). It allows users to align recordings even if there is non-linear drift of several minutes between them. A call detection and assignment pipeline can be used to find vocalisations and assign them to the vocalising individuals (even if the vocalisation is picked up on multiple microphones). The tracing and measurement functions allow for detailed analysis of the vocalisations and filtering of noise. Finally, the package includes a function to run spectrographic cross correlation, which can be used to compare vocalisations. It also includes multiple other functions related to analysis of vocal behaviour.

Multivariate Sparse Group Lasso for the Multivariate Multiple Linear Regression with an Arbitrary Group Structure

For fitting multivariate response and multiple predictor linear regressions with an arbitrary group structure assigned on the regression coefficient matrix, using the multivariate sparse group lasso and the mixed coordinate descent algorithm.

K-Means Clustering with Build-in Missing Data Imputation

This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details.

Miscellaneous Functions for "Grid" Graphics

Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.