LLD is a high-performance linker, built as a set of reusable components which highly leverage existing libraries in the larger LLVM Project.

This package provides a chat package connecting to API endpoints by OpenAI (<https://platform.openai.com/>) to answer questions (about R).

This package provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) <doi:10.1198/016214501753208690>.

Datasets relating to population in municipalities, municipality/county matching, and how different municipalities have merged/redistricted over time from 2006 to 2024.

Posterior inference under the convex mixture regression (CoMiRe) models introduced by Canale, Durante, and Dunson (2018) <doi:10.1111/biom.12917>.

Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. (2022) <doi:10.1371/journal.pcbi.1010618>.

This package provides tools to automate external pharmacokinetic model evaluation workflows, including Bayesian forecasting, predictive performance metrics, diagnostic plotting, and automated reporting.

Access small example datasets from Luquillo, a ForestGEO site in Puerto Rico (<https://forestgeo.si.edu/sites/north-america/luquillo>).

R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).

Gaussian-means (G-means) clustering is a clustering algorithm that extends the k-means algorithm by automatically determining the number of clusters.

Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines.

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

This package provides methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Calculate different metrics based on aquatic macroinvertebrate density data (individuals per square meter) to assess water quality (Prat N et al. 2009).

An unofficial wrapper for okx exchange v5 API <https://www.okx.com/docs-v5/en/>, including REST API and WebSocket API.

Provide access to QUANTkiosk platform of regulatory and corporate data relevant to financial markets including point-in-time fundamentals, ownership and symbology.

This package implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.

This package provides a time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.

Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data. vowels is the backend for the NORM website.

This package provides functions for manipulation of R documentation objects, including functions reprompt() and ereprompt() for updating Rd documentation for functions, methods and classes; it also includes Rd macros for citations and import of references from bibtex files for use in Rd files and roxygen2 comments, as well as many functions for manipulation of references and Rd files.

This package provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The GeoDa software and its documentation are available at <https://geodacenter.github.io>.