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Covariate Balancing Propensity Score
Implements the covariate balancing propensity score (CBPS) proposed
by Imai and Ratkovic (2014)
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.
Generalized Propensity Score Estimation and Matching for Multiple Groups
Implements the Vector Matching algorithm to match multiple
treatment groups based on previously estimated generalized propensity
scores. The package includes tools for visualizing initial confounder
imbalances, estimating treatment assignment probabilities using various
methods, defining the common support region, performing matching across
multiple groups, and evaluating matching quality. For more details, see
Lopez and Gutman (2017)