Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.
Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages distr', distrEx', distrMod', RobAStBase', and ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi10.1080/02331888.2011.628022 and \doi10.1007/s00184-011-0366-4.
Bridges the pharmaverse clinical reporting stack and the R4SUB (Ready for Submission) ecosystem. Converts metacore metadata objects and ADaM (Analysis Data Model) datasets - such as those built with admiral - into standardized R4SUB evidence table rows via r4subcore', so that submission readiness can be scored with r4subscore without changing an existing pharmaverse pipeline.
Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple redistverse packages at once. The redistverse is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.
Quickly find motif matches for many motifs and many sequences. This package wraps C++ code from the MOODS motif calling library.
This package provides themes for use with Shiny. It includes several Bootstrap themes, which are packaged for use with Shiny applications.
Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort.
Capybara is an integration testing tool for rack based web applications. It simulates how a user would interact with a website.
Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling.
Simulation, estimation and forecasting of first-order Beta-Skew-t-EGARCH models with leverage (one-component, two-component, skewed versions).
Displays for model fits of multiple models and their ensembles. For classification models, the plots are heatmaps, for regression, scatterplots.
Automatic generation of quizzes or individual questions as (interactive) forms within rmarkdown or quarto documents based on R/exams exercises.
Implementation to perform forecasting of locally stationary wavelet processes by examining the local second order structure of the time series.
Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.
Split your rmarkdown or quarto files by sections into a tibble: titles, text, chunks. Rebuild the file from the tibble.
Programmatically collect normalized news from (almost) any website. An R clone of the <https://github.com/kotartemiy/newscatcher> Python module.
This R package allows the determination of some distributions of the voters power when passing laws in weighted voting situations.
Extract and interact with data from the Scottish Health and Social Care Open Data platform <https://www.opendata.nhs.scot>.
Collect your data on digital marketing campaigns from Shopify Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.
Collect your data on digital marketing campaigns from Twitter Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.
This package provides functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
GitLab Runner is the open source project that is used to run your jobs and send the results back to GitLab.