An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data.

There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants.

Generates data for challenging machine learning models in Arena <https://arena.drwhy.ai> - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable Arena URL.

The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.

This package provides functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.

This package implements bidirectional two-stage least squares (Bi-TSLS) estimation for identifying bidirectional causal effects between two variables in the presence of unmeasured confounding. The method uses proxy variables (negative control exposure and outcome) along with at least one covariate to handle confounding.

An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>). Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

This package provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

This package provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.

This package provides methods for interpretable machine learning with an emphasis on local feature importance estimation. The package implements the CLIQUE framework for computing observation-level importance values. Additional tools are included for visualizing multi-class partial dependence relationships across predictors and response classes.

This package provides functions for comparing two data.frames against each other. The core functionality is to provide a detailed breakdown of any differences between two data.frames as well as providing utility functions to help narrow down the source of problems and differences.

In order to provide unified access to Linux distribution details in R, this package wraps the various files and commands that may exist on a system. It is similar in spirit to the lsb_release command and the Python package of the same name.

Computes and plots a transformed empirical CDF (ecdf) as a diagnostic for heavy tailed data, specifically data with power law decay on the tails. Routines for annotating the plot, comparing data to a model, fitting a nonparametric model, and some multivariate extensions are given.

Functional principal component analysis under the Linear Mixed Models representation of smoothing splines. The method utilizes the Demmler-Reinsch basis and assumes error independence. For more details see: F. Rosales (2016) <https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-87F9-6>.

Fuzzy clustering of species in an ecological community as common or rare based on their abundance and occupancy. It also includes functions to compute confidence intervals of classification metrics and plot results. See Balbuena et al. (2020, <doi:10.1101/2020.08.12.247502>).

The groupr package provides a more powerful version of grouped tibbles from dplyr'. It allows groups to be marked inapplicable, which is a simple but widely useful way to express structure in a dataset. It also provides powerful pivoting and other group manipulation functions.

Enhance a mice imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce ggplot objects which may be easily manipulated or extended. Use ggmice to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.

Companion package for the manual guide-R : Guide pour lâ analyse de données dâ enquêtes avec R available at <https://larmarange.github.io/guide-R/>. guideR implements miscellaneous functions introduced in guide-R to facilitate statistical analysis and manipulation of survey data.

This package provides tools to download, process, and analyze data from the International Monetary Fund's World Economic Outlook (WEO) <https://www.imf.org/en/publications/weo>. Functions support downloading complete WEO releases, accessing specific economic indicators for selected countries, and listing available data.

Template engine powered by the inja C++ library. Users write a template document, using syntax inspired by the Jinja Python package, and then render the final document by passing data from R. The template syntax supports features such as variables, loops, conditions and inheritance.

Clustering typically assigns data points into discrete groups, but the clusters can sometimes be indistinct. Cluster sharpening adjusts an existing clustering to create contrast between groups. This package provides a general interface for cluster sharpening along with several implementations based on different excision criteria.

Functionality for generating and plotting random mazes. The mazes are based on matrices, so can only consist of vertical and horizontal lines along a regular grid. But there is no need to use every possible space, so they can take on many different shapes.

This package provides a simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.