Generates synthetic Our Future Health cohort datasets for method development, including participant, questionnaire, clinic measurements, outpatient, inpatient, emergency, mortality, primary care medication, and geography outputs. Supports reproducible generation with configurable cohort size and user-defined International Classification of Diseases, Tenth Revision (ICD-10), Office of Population Censuses and Surveys Classification of Interventions and Procedures, version 4 (OPCS-4), and British National Formulary (BNF) code pools.

Construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis as described in Szabo, A. and Boucher, K (2002) <doi:10.1016/S0025-5564(02)00086-X>.

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 Welfare Trends Report (incapacity benefit spending and caseloads), and the Fiscal Risks and Sustainability Report (50-year state pension projections). 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 functions for optimal policy learning in socioeconomic applications helping users to learn the most effective policies based on data in order to maximize empirical welfare. Specifically, OPL allows to find "treatment assignment rules" that maximize the overall welfare, defined as the sum of the policy effects estimated over all the policy beneficiaries. Documentation about OPL is provided by several international articles via Athey et al (2021, <doi:10.3982/ECTA15732>), Kitagawa et al (2018, <doi:10.3982/ECTA13288>), Cerulli (2022, <doi:10.1080/13504851.2022.2032577>), the paper by Cerulli (2021, <doi:10.1080/13504851.2020.1820939>) and the book by Gareth et al (2013, <doi:10.1007/978-1-4614-7138-7>).

Extracts covariates from Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) domains using an R-only pipeline. Supports configurable temporal windows, domain-specific covariates for drug exposure, drug era (including Anatomical Therapeutic Chemical (ATC) groupings), condition occurrence, condition era, concept sets and cohorts. Methods are based on the Observational Health Data Sciences and Informatics (OHDSI) framework described in Hripcsak et al. (2015) <doi:10.1038/sdata.2015.35> and "The Book of OHDSI" OHDSI (2019, ISBN:978-1-7923-0589-8).

This package provides a collection of functions that aid in calculating the optimum time to stock hatchery reared fish into a body of water given the growth, mortality and cost of raising a particular number of individuals to a certain length.

Generalise the starting point of the array index.

This package provides utilities for processing of Oxy-Bisulfite microarray data (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) with tandem arrays, one using conventional bisulfite conversion, the other using oxy-bisulfite conversion.

R bindings to odiff', a blazing-fast pixel-by-pixel image comparison tool <https://github.com/dmtrKovalenko/odiff>. Supports PNG, JPEG, WEBP, and TIFF with configurable thresholds, antialiasing detection, and region ignoring. Requires system installation of odiff'. Ideal for visual regression testing in automated workflows.

Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package alabama', or by sequential quadratic programming using solver slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package cccp'.

Uses the outputs of a logistic regression model, from caret <https://CRAN.R-project.org/package=caret>, to build an odds plot. This allows for the rapid visualisation of odds plot ratios and works best with the outputs of CARET's GLM model class, by returning the final trained model.

Shiny UI to identify cliques of related constructs in repertory grid data. See Burr, King, & Heckmann (2020) <doi:10.1080/14780887.2020.1794088> for a description of the interpretive clustering (IC) method.

Create R plots visualising ontological terms and the relationships between them with various graphical options - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.

The ordered panel methodology (Zezulinski et al 2025 <doi:10.1159/000545366>) provides a structured framework for identifying and organizing sets of biomarkers, such as genetic variants, that distinguish between positive and negative subjects in a study when only a training cohort is available. This approach is particularly useful in situations where an independent validation cohort does not yet exist, rendering conventional performance metrics such as the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) inappropriate or potentially misleading. The methodology emphasizes transparent construction and evaluation of ordered signatures of biomarkers, allowing investigators to examine operating characteristics without establishing predictive performance.

This package provides a decision support tool for prioritizing conservation projects. Prioritizations can be developed by maximizing expected feature richness, expected phylogenetic diversity, the number of features that meet persistence targets, or identifying a set of projects that meet persistence targets for minimal cost. Constraints (e.g. lock in specific actions) and feature weights can also be specified to further customize prioritizations. After defining a project prioritization problem, solutions can be obtained using exact algorithms, heuristic algorithms, or random processes. In particular, it is recommended to install the Gurobi optimizer (available from <https://www.gurobi.com>) because it can identify optimal solutions very quickly. The rcbc R package (available at <https://github.com/dirkschumacher/rcbc>) can also be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). Finally, methods are provided for comparing different prioritizations and evaluating their benefits. For more information, see Hanson et al. (2019) <doi:10.1111/2041-210X.13264>.

The aim of od is to provide tools and example datasets for working with origin-destination ('OD') datasets of the type used to describe aggregate urban mobility patterns (Carey et al. 1981) <doi:10.1287/trsc.15.1.32>. The package builds on functions for working with OD data in the package stplanr', (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053> with a focus on computational efficiency and support for the sf class system (Pebesma 2018) <doi:10.32614/RJ-2018-009>. With few dependencies and a simple class system based on data frames, the package is intended to facilitate efficient analysis of OD datasets and to provide a place for developing new functions. The package enables the creation and analysis of geographic entities representing large scale mobility patterns, from daily travel between zones in cities to migration between countries.

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

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

This package provides a collection of numerical optimization algorithms. One is a simple implementation of the primitive grid search algorithm, the other is an extension of the simulated annealing algorithm that can take custom boundaries into account. The methodology for this bounded simulated annealing algorithm is due to Haario and Saksman (1991), <doi:10.2307/1427681>.

Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.

This package provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.

Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.

Set of tools to generate samples of k-th order statistics and others quantities of interest from new families of distributions. The main references for this package are: C. Kleiber and S. Kotz (2003) Statistical size distributions in economics and actuarial sciences; Gentle, J. (2009), Computational Statistics, Springer-Verlag; Naradajah, S. and Rocha, R. (2016), <DOI:10.18637/jss.v069.i10> and Stasinopoulos, M. and Rigby, R. (2015), <DOI:10.1111/j.1467-9876.2005.00510.x>. The families of distributions are: Benini distributions, Burr distributions, Dagum distributions, Feller-Pareto distributions, Generalized Pareto distributions, Inverse Pareto distributions, The Inverse Paralogistic distributions, Marshall-Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, exponentiated exponential Poisson G distributions, Poisson-inverse gaussian distributions, Skew normal type 1 distributions, Skew student t distributions, Singh-Maddala distributions, Sinh-Arcsinh distributions, Sichel distributions, Zero inflated Poisson distributions.

An optimized method for distribution-preserving class-proportional down-sampling of bio-medical data <doi:10.1371/journal.pone.0255838>.