Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package glmnet in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.

This package provides functions to efficiently query ArcGIS REST APIs <https://developers.arcgis.com/rest/>. Both spatial and SQL queries can be used to retrieve data. Simple Feature (sf) objects are utilized to perform spatial queries. This package was neither produced nor is maintained by Esri.

This package provides functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this afthd package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) <doi:10.21307/stattrans-2016-046>.

Simulates battles in the board game Axis and Allies Spring 1942, and calculates your probability of winning a battle. This speeds the game up significantly.

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the pamr <http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the pamr algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.

This package creates all leave-one-out models and produces predictions for test samples.

La libreria ACEP contiene funciones especificas para desarrollar analisis computacional de eventos de protesta. Asimismo, contiene base de datos con colecciones de notas sobre protestas y diccionarios de palabras conflictivas. Coleccion de diccionarios que reune diccionarios de diferentes origenes. The ACEP library contains specific functions to perform computational analysis of protest events. It also contains a database with collections of notes on protests and dictionaries of conflicting words. Collection of dictionaries that brings together dictionaries from different sources.

This package contains tools to fit the additive hazards model to data from a cohort, random sampling, two-phase Bernoulli sampling and two-phase finite population sampling, as well as calibration tool to incorporate phase I auxiliary information into the two-phase data model fitting. This package provides regression parameter estimates and their model-based and robust standard errors. It also offers tools to make prediction of individual specific hazards.

Stanford ATLAS (Advanced Temporal Search Engine) is a powerful tool that allows constructing cohorts of patients extremely quickly and efficiently. This package is designed to interface directly with an instance of ATLAS search engine and facilitates API queries and data dumps. Prerequisite is a good knowledge of the temporal language to be able to efficiently construct a query. More information available at <https://shahlab.stanford.edu/start>.

Package to incorporate change point analysis in ARIMA forecasting.

This package provides a software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.

Collect your data on digital marketing campaigns from Apple Search Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a toolbox to read all R files inside a package and automatically generate @importFrom roxygen2 tags in the right place. Includes a shiny application to review the changes before applying them.

This package provides a collection of tools for the analysis of animal movements.

Easy-to-use tools for performing complex queries on avidaDB', a semantic database that stores genomic and transcriptomic data of self-replicating computer programs (known as digital organisms) that mutate and evolve within a user-defined computational environment.

Adaptive and Robust Transfer Learning (ART) is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye (2023) <doi:10.1002/sta4.582>.

Implementation of the Artificial Hydrocarbon Networks for data modeling.

This package provides functions to produce accessible HTML slides, HTML', Word and PDF documents from input R markdown files. Accessible PDF files are produced only on a Windows Operating System. One aspect of accessibility is providing a headings structure that is recognised by a screen reader, providing a navigational tool for a blind or partially-sighted person. A key aim is to produce documents of different formats easily from each of a collection of R markdown source files. Input R markdown files are rendered using the render() function from the rmarkdown package <https://cran.r-project.org/package=rmarkdown>. A zip file containing multiple output files can be produced from one function call. A user-supplied template Word document can be used to determine the formatting of an output Word document. Accessible PDF files are produced from Word documents using OfficeToPDF <https://github.com/cognidox/OfficeToPDF>. A convenience function, install_otp() is provided to install this software. The option to print HTML output to (non-accessible) PDF files is also available.

Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.

ATPOL is a rectangular grid system used for botanical studies in Poland. The ATPOL grid was developed in Institute of Botany, Jagiellonian University, Krakow, Poland in 70. Since then it is widely used to represent distribution of plants in Poland. atpolR provides functions to translate geographic coordinates to the grid and vice versa. It also allows to create a choreograph map.

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from Seurat objects, for GRN analysis by ANANSE implemented in snakemake'. Finally, incorporate results for visualization and interpretation.

Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction <doi:10.1080/01621459.2025.2571245>, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.

This package provides algorithms for creating artworks in the ggplot2 language that incorporate some form of randomness.

Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in NONMEM (non-linear mixed effects modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions to build datasets from SDTM (study data tabulation module) <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication.