This package provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Package for the access and distribution of long-term lake datasets from 28 lakes in the Adirondack Park, northern New York state. Includes a wide variety of physical, chemical, and biological parameters originally described in Farrell et al. 2018 <doi:10.1038/sdata.2018.59>. Water chemistry and nutrient records are extended through 2024 using data from the USGS AQ Samples database, including new columns for surface temperature, UV-254 absorbance, and a program flag distinguishing AEAP integrated samples from ALTM surface grabs. The underlying figshare archive <doi:10.6084/m9.figshare.32305479> additionally contains chemistry records for 25 ALTM-only lakes; the package restricts to the 28 originals for consistency with the published dataset.

Fetch Australian Energy Market Operator (AEMO) public data from NEMweb <http://nemweb.com.au> and the Market Management System Data Model (MMSDM) historical archive. Provides tidy access to 5-minute and 30-minute wholesale electricity prices, regional demand, dispatch-unit output, interconnector flows, rooftop photovoltaic generation, generator bids, predispatch forecasts, frequency control ancillary services markets, and gas market data across the National Electricity Market (NEM) regions. Data is published by AEMO under its Copyright Permissions Notice <https://www.aemo.com.au/privacy-and-legal-notices/copyright-permissions>.

Some convenient functions to work with arrays.

This package implements inverse probability weighted, regression adjustment, and multiply robust estimators under the available complete-case missing value assumption of Cheng, Chen, Smith, and Zhao (2022) <doi:10.48550/arXiv.2207.02289>. Supports one or two primary variables, exponential-tilt sensitivity analysis, regression weights, and nonparametric bootstrap confidence intervals.

This package provides a cross-platform R framework that facilitates processing of any number of Affymetrix microarray samples regardless of computer system. The only parameter that limits the number of chips that can be processed is the amount of available disk space. The Aroma Framework has successfully been used in studies to process tens of thousands of arrays. This package has actively been used since 2006.

This package provides a high-performance, flexible and extensible framework to develop continuous-time agent based models. Its high performance allows it to simulate millions of agents efficiently. Agents are defined by their states (arbitrary R lists). The events are handled in chronological order. This avoids the multi-event interaction problem in a time step of discrete-time simulations, and gives precise outcomes. The states are modified by provided or user-defined events. The framework provides a flexible and customizable implementation of state transitions (either spontaneous or caused by agent interactions), making the framework suitable to apply to epidemiology and ecology, e.g., to model life history stages, competition and cooperation, and disease and information spread. The agent interactions are flexible and extensible. The framework provides random mixing and network interactions, and supports multi-level mixing patterns. It can be easily extended to other interactions such as inter- and intra-households (or workplaces and schools) by subclassing an R6 class. It can be used to study the effect of age-specific, group-specific, and contact- specific intervention strategies, and complex interactions between individual behavior and population dynamics. This modeling concept can also be used in business, economical and political models. As a generic event based framework, it can be applied to many other fields. More information about the implementation and examples can be found at <https://github.com/junlingm/ABM>.

This package contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Streamline use of the All of Us Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the All of Us database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of All of Us tools and allowing for cohort creation via Atlas'. Improve reproducible and transparent research using All of Us'.

Generate spreadsheet publications that follow best practice guidance from the UK government's Analysis Function, available at <https://analysisfunction.civilservice.gov.uk/policy-store/releasing-statistics-in-spreadsheets/>, with a focus on accessibility. See also the Python package gptables'.

With the functions in this package you can check the validity of the Greek Tax Identification Number (AFM) and the Greek Personal Number (PA) <https://pa.gov.gr>. The PA is a new universal ID for Greek citizens across all public services and it is to replace older numbers issued by various Greek state agencies. Its format is a 12-character ID consisting of three alphanumeric characters followed by the nine numerical digits of the AFM.

This package implements the framework of Tiwari and Majumdar (2025) <doi:10.48550/arXiv.2512.07154> for valuing arithmetic and geometric Asian options under transient and permanent market impact. Provides three pricing approaches: Kemna-Vorst frictionless benchmarks, exogenous diffusion pricing (closed-form for geometric, Monte Carlo for arithmetic), and endogenous Hamilton-Jacobi-Bellman valuation via a tree-based Bellman scheme producing indifference bid-ask prices.

This package creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.

We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.

This package provides a Shiny application to access the functionalities and datasets of the archeofrag package for spatial analysis in archaeology from refitting data. Quick and seamless exploration of archaeological refitting datasets, focusing on physical refits only. Features include: built-in documentation and convenient workflow, plot generation and export, anomaly detection in the spatial distribution of refitting connection, exploration of spatial units merging solutions, data export to the fabryka application for spatial orientation analysis, simulation of archaeological site formation processes, support for parallel computing, R code generation to re-execute simulations and ensure reproducibility, code generation for the openMOLE model exploration software. A demonstration of the app is available at <https://analytics.huma-num.fr/archeofrag/>.

Implementation of a hybrid MCDM method build from the AHP (Analytic Hierarchy Process) and TOPSIS-2N (Technique for Order of Preference by Similarity to Ideal Solution - with two normalizations). This method is described in Souza et al. (2018) <doi: 10.1142/S0219622018500207>.

Automated Characterization of Health Information at Large-Scale Longitudinal Evidence Systems. Creates a descriptive statistics summary for an Observational Medical Outcomes Partnership Common Data Model standardized data source. This package includes functions for executing summary queries on the specified data source and exporting reporting content for use across a variety of Observational Health Data Sciences and Informatics community applications.

This is a simple and powerful package to create, render, preview, and deploy documentation websites for R packages. It is a lightweight and flexible alternative to pkgdown', with support for many documentation generators, including Quarto', Docute', Docsify', and MkDocs'.

This package provides ANOCVA (ANalysis Of Cluster VAriability), a non-parametric statistical test to compare clustering structures with applications in functional magnetic resonance imaging data (fMRI). The ANOCVA allows us to compare the clustering structure of multiple groups simultaneously and also to identify features that contribute to the differential clustering.

An implementation of the ALFAM2 dynamic emission model for ammonia volatilization from field-applied animal slurry (manure with dry matter below about 15%). The model can be used to predict cumulative emission and emission rate of ammonia following field application of slurry. Predictions may be useful for emission inventory calculations, fertilizer management, assessment of mitigation strategies, or research aimed at understanding ammonia emission. Default parameter sets include effects of application method, slurry composition, and weather. The model structure is based on a simplified representation of the physical-chemical slurry-soil-atmosphere system. More information is available via citation("ALFAM2").

Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

This package provides functions to compute various clinical scores used in healthcare. These include the Charlson Comorbidity Index (CCI), predicting 10-year survival in patients with multiple comorbidities; the EPICES score, an individual indicator of precariousness considering its multidimensional nature; the MELD score for chronic liver disease severity; the Alternative Fistula Risk Score (a-FRS) for postoperative pancreatic fistula risk; and the Distal Pancreatectomy Fistula Risk Score (D-FRS) for risk following distal pancreatectomy. For detailed methodology, refer to Charlson et al. (1987) <doi:10.1016/0021-9681(87)90171-8> , Sass et al. (2006) <doi:10.1007/s10332-006-0131-5>, Kamath et al. (2001) <doi:10.1053/jhep.2001.22172>, Kim et al. (2008) <doi:10.1056/NEJMoa0801209> Kim et al. (2021) <doi:10.1053/j.gastro.2021.08.050>, Mungroop et al. (2019) <doi:10.1097/SLA.0000000000002620>, and de Pastena et al. (2023) <doi:10.1097/SLA.0000000000005497>..

RStudio allows to show and navigate for the outline of a R Markdown file, but not for R Markdown projects with multiple files. For this reason, I have developed several RStudio addins capable of show project outline. Each addin is specialized in showing projects of different types: R Markdown project, bookdown package project and LaTeX project. There is a configuration file that allows you to customize additional searches.

This package implements anchored geodesic component analysis for multivariate extremes. The package provides Pareto and rank-Pareto marginal standardization, top-k angular extraction, anchored eigensolutions, reconstruction, diagnostics, nonparametric bootstrap tools, and plotting methods for benchmark-relative angular variation.