Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the GritBot <https://www.rulequest.com/gritbot-info.html> software.

An integrated R interface to the Overture API (<https://docs.overturemaps.org/>). Allows R users to return Overture data as dbplyr data frames or materialized sf spatial data frames.

This package provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

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

This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where â 0â may stand for negative and â 1â for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).

Supports the modeling of ordinal random variables, like the outcomes of races, via Softmax regression, under the Harville <doi:10.1080/01621459.1973.10482425> and Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

This package provides a DBI-compatible interface to ODBC databases.

This package provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.

This package provides tools for collecting municipal-level data <http://www.transparencia.gov.br/swagger-ui.html> from several Brazilian governmental social programs.

Estimates rates for continuous character evolution under Brownian motion and Ornstein-Uhlenbeck based Hansen models that allow both the strength of the pull and stochastic motion to vary across selective regimes. Beaulieu et al. (2012).

Provide methods for estimating optimal treatment regimes in survival contexts with Kaplan-Meier-like estimators when no unmeasured confounding assumption is satisfied (Jiang, R., Lu, W., Song, R., and Davidian, M. (2017) <doi:10.1111/rssb.12201>) and when no unmeasured confounding assumption fails to hold and a binary instrument is available (Xia, J., Zhan, Z., Zhang, J. (2022) <arXiv:2210.05538>).

This package provides dates for public and school holidays for a number of countries and their subdivisions through the OpenHolidays API at <https://www.openholidaysapi.org/en/>.

Solves penalized least squares problems for big tall data using the orthogonalizing EM algorithm of Xiong et al. (2016) <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the functions cv.oem() and xval.oem() are for cross validation, the latter being an accelerated cross validation function for linear models. The big.oem() function allows for out of memory fitting. A description of the underlying methods and code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.

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

This package provides a utility to quickly obtain clean and tidy sports odds from The Odds API <https://the-odds-api.com>. Provides wrappers for every version 4 endpoint -- featured-market and single-event odds (including player props and alternate lines), historical odds snapshots, scores, events, participants, and usage-quota reporting -- returning tidy tibbles ready for analysis.

This package provides a derivative-based optimization framework that allows users to combine eight convergence criteria. Unlike standard optimization functions, this package includes a built-in mechanism to verify the positive definiteness of the Hessian matrix at the point of convergence. This additional check helps prevent the solver from falsely identifying non-optimal solutions, such as saddle points, as valid minima.

This package provides a collection of aesthetically appealing color palettes for effective data visualization with ggplot2'. Palettes support both discrete and continuous data.

Social media sites often embed cards when links are shared, based on metadata in the Open Graph Protocol (<https://ogp.me/>). This supports extracting that metadata from a website. It further allows for the creation of tags to add to a website to support the Open Graph Protocol and provides a list of the standard tags and their required properties.

Facilitates the gathering of biodiversity occurrence data from disparate sources. Metadata is managed throughout the process to facilitate reporting and enhanced ability to repeat analyses.

This package provides native access to the Open Neural Network Exchange (ONNX) Runtime <https://onnxruntime.ai/>, which is a performant engine for running machine learning models that are saved to a standardized format. Rather than interfacing with ONNX via Python', as in the official onnx package, onnxr directly interfaces with the runtime's C++ API via cpp11'. Models saved to .onnx files can be loaded and run on various backends, including CPUs and Apple's CoreML library.

OpenTelemetry is a collection of tools, APIs, and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package contains the OpenTelemetry SDK, and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry.

Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2012) <DOI:10.1080/01621459.2011.646925>, Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, Sun, Zhu, Wang & Zeng (2019) <DOI:10.1093/biomet/asy064> and Zhou, Zhu & Zeng (2021) <DOI:10.1093/biomet/asaa087>. The package also implements some existing dimension reduction methods such as hMave by Xia, Zhang, & Xu (2010) <DOI:10.1198/jasa.2009.tm09372> and partial SAVE by Feng, Wen & Zhu (2013) <DOI:10.1080/01621459.2012.746065>. It also serves as a general purpose optimization solver for problems with orthogonality constraints, i.e., in Stiefel manifold. Parallel computing for approximating the gradient is enabled through OpenMP'.

This package provides functions to design and simulate optimal two-stage randomized controlled trials (RCTs) with ordered categorical outcomes, using rank-based tests and interim decision rules for futility and superiority, following Park (2025) <doi:10.1371/journal.pone.0318211>. The functions rule()', op() and design_table() provide a single entry point for constructing designs, evaluating their operating characteristics, and tabulating several designs at once. The separate functions provided for each combination of test statistic and stopping rule up to version 1.0.2 were deprecated in 1.0.3 and are removed here. Please see the package reference manual and the vignette for details.