Facilitates the creation of intuitive figures to describe metabolomics data by utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) hierarchy data, and gathers functional orthology and gene data from the KEGG-REST API.

This package provides a set of binary operators for common tasks such as regex manipulation.

Bindings, methods, and tuners for using ordinal classification models with the parsnip and dials packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) <doi:10.18637/jss.v099.i06> in ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) <doi:10.18637/jss.v047.i10> in rpartScore', and the latent variable ordinal forests of Hornung (2020) <doi:10.1007/s00357-018-9302-x> in ordinalForest'.

Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association for Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2023) <https://aapor.org/wp-content/uploads/2024/03/Standards-Definitions-10th-edition.pdf>.

This package provides clean, tidy access to data published by the Office for Budget Responsibility ('OBR'), the UK's independent fiscal watchdog. Covers the Public Finances Databank (outturn for PSNB, PSND, receipts, and expenditure since 1946), the Historical Official Forecasts Database (every OBR forecast since 2010), the Economic and Fiscal Outlook detailed forecast tables (five-year projections from the latest Budget), the monthly profiles for the public finances (the OBR forecast apportioned across the months of the fiscal year), and the Welfare Trends Report (incapacity benefit spending and caseloads). All returned objects carry provenance metadata recording the source URL, publication vintage, retrieval time, and file fingerprint, so analyses can be audited and reproduced. Data is downloaded from the OBR on first use and cached locally for subsequent calls. Data is sourced from the OBR website <https://obr.uk>.

This package provides tools for processing and analyzing data from the O-GlcNAcAtlas database <https://oglcnac.org/>, as described in Ma (2021) <doi:10.1093/glycob/cwab003>. It integrates UniProt <https://www.uniprot.org/> API calls to retrieve additional information. It is specifically designed for research workflows involving O-GlcNAcAtlas data, providing a flexible and user-friendly interface for customizing and downloading processed results. Interactive elements allow users to easily adjust parameters and handle various biological datasets.

Predictive scores must be updated with care, because actions taken on the basis of existing risk scores causes bias in risk estimates from the updated score. A holdout set is a straightforward way to manage this problem: a proportion of the population is held-out from computation of the previous risk score. This package provides tools to estimate a size for this holdout set and associated errors. Comprehensive vignettes are included. Please see: Haidar-Wehbe S, Emerson SR, Aslett LJM, Liley J (2022) <doi:10.48550/arXiv.2202.06374> (in Annals of Applied Statistics) for details of methods.

Generate systems of ordinary differential equations (ODE) and integrate them, using a domain specific language (DSL). The DSL uses R's syntax, but compiles to C in order to efficiently solve the system. A solver is not provided, but instead interfaces to the packages deSolve and dde are generated. With these, while solving the differential equations, no allocations are done and the calculations remain entirely in compiled code. Alternatively, a model can be transpiled to R for use in contexts where a C compiler is not present. After compilation, models can be inspected to return information about parameters and outputs, or intermediate values after calculations. odin is not targeted at any particular domain and is suitable for any system that can be expressed primarily as mathematical expressions. Additional support is provided for working with delays (delay differential equations, DDE), using interpolated functions during interpolation, and for integrating quantities that represent arrays.

Growing collection of helper functions for point pattern analysis. Most functions are designed to work with the spatstat (<http://spatstat.org>) package. The focus of most functions are either null models or summary functions for spatial point patterns. For a detailed description of all null models and summary functions, see Wiegand and Moloney (2014, ISBN:9781420082548).

This package provides an interface to connect R with the <https://github.com/IDEMSInternational/open-app-builder> OpenAppBuilder platform, enabling users to retrieve and work with user and notification data for analysis and processing. It is designed for developers and analysts to seamlessly integrate data from OpenAppBuilder into R workflows via a Postgres database connection, allowing direct querying and import of app data into R.

This package provides functions to access and download data from the Open Case Studies <https://www.opencasestudies.org/> repositories on GitHub <https://github.com/opencasestudies>. Different functions enable users to grab the data they need at different sections in the case study, as well as download the whole case study repository. All the user needs to do is input the name of the case study being worked on. The package relies on the httr::GET() function to access files through the GitHub API. The functions usethis::use_zip() and usethis::create_from_github() are used to clone and/or download the case study repositories. To cite an individual case study, please see the respective README file at <https://github.com/opencasestudies/>. <https://github.com/opencasestudies/ocs-bp-rural-and-urban-obesity> <https://github.com/opencasestudies/ocs-bp-air-pollution> <https://github.com/opencasestudies/ocs-bp-vaping-case-study> <https://github.com/opencasestudies/ocs-bp-opioid-rural-urban> <https://github.com/opencasestudies/ocs-bp-RTC-wrangling> <https://github.com/opencasestudies/ocs-bp-RTC-analysis> <https://github.com/opencasestudies/ocs-bp-youth-disconnection> <https://github.com/opencasestudies/ocs-bp-youth-mental-health> <https://github.com/opencasestudies/ocs-bp-school-shootings-dashboard> <https://github.com/opencasestudies/ocs-bp-co2-emissions> <https://github.com/opencasestudies/ocs-bp-diet>.

An implementation of the Blinder-Oaxaca decomposition for linear regression models.

Fits regularised multi-task learning models where relationships between tasks are controlled via orthogonality or disjoint-support constraints. Supports regression, binary classification, and censored survival data. In survival mode, time-to-event outcomes are converted into binary labels at user-defined thresholds, enabling the discovery of features with time-varying effects that standard proportional-hazards models cannot detect. Implements the penalty described in Vervier et al. (2014) <https://hal.science/hal-00985654>.

Assessment and diagnostics for comparing competing clustering solutions, using predictive models. The main intended use is for comparing clustering/classification solutions of ecological data (e.g. presence/absence, counts, ordinal scores) to 1) find an optimal partitioning solution, 2) identify characteristic species and 3) refine a classification by merging clusters that increase predictive performance. However, in a more general sense, this package can do the above for any set of clustering solutions for i observations of j variables.

An interface to the search API of HAL <https://hal.science/>, the French open archive for scholarly documents from all academic fields. This package provides programmatic access to the API <https://api.archives-ouvertes.fr/docs> and allows to search for records and download documents.

Computes optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.

Data processing, visualisation and analysis of Limit Order Book event data.

This package provides a collection of general optimization routines based on variants of the One Side Cut Accelerated Random Search (OSCARS-II) algorithm (Price et al., 2020, <doi:10.1007/s10898-020-00928-6>). The main function , oscars()', performs black-box optimization of a general (including nonsmooth or discontinuous) function subject to simple bounds on the unknowns. If all bounds are finite, oscars searches globally. The main method implements a stochastic direct search method and is derivative free. Testing shows the OSCARS-II algorithm usually finds extrema with fewer function evaluations than similar global derivative-free methods.

Plotting toolbox for 2D oceanographic data (satellite data, sea surface temperature, chlorophyll, ocean fronts & bathymetry). Recognized classes and formats include netcdf, Raster, .nc and .gz files.

Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2024) <doi:10.1016/j.ins.2024.121133>.

This package provides tools for epidemiological analysis of disease outbreaks, including measures of disease frequency, association, impact, transmission, and vaccine effectiveness. Functions support prevalence, incidence, attack rates, mortality, case fatality, risk ratios, odds ratios, rate ratios, attributable measures, reproduction numbers, herd immunity thresholds, contingency tables, grouped analyses, and outbreak line-list validation.

Potential outliers are identified for all combinations of a dataset's variables. O3 plots are described in Unwin(2019) <doi:10.1080/10618600.2019.1575226>. The available methods are FastPCS() from the package FastPCS', mvBACON() from robustX', adjOutlyingness() from robustbase', DectectDeviatingCells() from cellWise', covMcd() from robustbase'.

Several function related to Experimental Design are implemented here, see "Optimal Experimental Design with R" by Rasch D. et. al (ISBN 9781439816974).

Non-spatial and spatial open-population capture-recapture analysis.