Algorithms for D-, A-, I-, and c-optimal designs. For more details, see the package description. Some of the functions in this package require the gurobi software and its accompanying R package. For their installation, please follow the instructions at <https://www.gurobi.com> and the file gurobi_inst.txt, respectively.

For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiroâ Azócar A, Heath A, Baio G (2022) ``Parametric Gâ computation for compatible indirect treatment comparisons with limited individual patient data'', Res. Synth. Methods, 1â 31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

This package provides functions for detecting outliers in datasets using statistical methods. The package supports identification of anomalous observations in numerical data and is intended for use in data cleaning, exploratory data analysis, and preprocessing workflows.

This package provides a visualization tool for multivariate data. This package maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multi-criteria decision-making.

This package provides functions for the design process of survey sampling, with specific tools for multi-wave and multi-phase designs. Perform optimum allocation using Neyman (1934) <doi:10.2307/2342192> or Wright (2012) <doi:10.1080/00031305.2012.733679> allocation, split strata based on quantiles or values of known variables, randomly select samples from strata, allocate sampling waves iteratively, and organize a complex survey design. Also includes a Shiny application for observing the effects of different strata splits. A paper on this package was published in the Journal of Statistical Software <doi:10.18637/jss.v114.i10>.

Apache OpenNLP jars and basic English language models.

Interact seamlessly with Open Target GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of gene, disease, drug, and genetic data. For more information about the Open Target API (<https://platform.opentargets.org/api>).

Flexible optimizer with numerous input specifications for detailed parameterisation. Designed for complex loss functions with state and parameter space constraints. Visualization tools for validation and analysis of the convergence are included.

An R wrapper for the OneMap.Sg API <https://www.onemap.gov.sg/docs/>. Functions help users query data from the API and return raw JSON data in "tidy" formats. Support is also available for users to retrieve data from multiple API calls and integrate results into single dataframes, without needing to clean and merge the data themselves. This package is best suited for users who would like to perform analyses with Singapore's spatial data without having to perform excessive data cleaning.

This package provides a set of commands to manage an abstract optimization method. The goal is to provide a building block for a large class of specialized optimization methods. This package manages: the number of variables, the minimum and maximum bounds, the number of non linear inequality constraints, the cost function, the logging system, various termination criteria, etc...

The online principal component regression method can process the online data set. OPCreg implements the online principal component regression method, which is specifically designed to process online datasets efficiently. This method is particularly useful for handling large-scale, streaming data where traditional batch processing methods may be computationally infeasible.The philosophy of the package is described in Guo (2025) <doi:10.1016/j.physa.2024.130308>.

This package provides details such as Morphine Equivalent Dose (MED), brand name and opioid content which are calculated of all oral opioids authorized for sale by Health Canada and the FDA based on their Drug Identification Number (DIN) or National Drug Code (NDC). MEDs are calculated based on recommendations by Canadian Institute for Health Information (CIHI) and Von Korff et al (2008) and information obtained from Health Canada's Drug Product Database's monthly data dump or FDA Daily database for Canadian and US databases respectively. Please note in no way should output from this package be a substitute for medical advise. All medications should only be consumed on prescription from a licensed healthcare provider.

Allows access to a proof-of-concept database containing Open Access species range models and relevant metadata. Access to the database is via both PostgreSQL connection and API <https://github.com/EnquistLab/Biendata-Frontend>, allowing diverse use-cases.

Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen and Klopfer 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.

This package provides functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.

Given a certain coverage level, obtains simultaneous confidence bands for the survival and cumulative hazard functions such that the area between is minimized. Produces an approximate solution based on local time arguments.

This package provides functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.

Interface to make HTTP requests to OpenBlender API services. Go to <https://openblender.io> for more information.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

Sequential outlier identification for Gaussian mixture models using the distribution of Mahalanobis distances. The optimal number of outliers is chosen based on the dissimilarity between the theoretical and observed distributions of the scaled squared sample Mahalanobis distances. Also includes an extension for Gaussian linear cluster-weighted models using the distribution of studentized residuals. Doherty, McNicholas, and White (2025) <doi:10.48550/arXiv.2505.11668>.

An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining).

An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established R formula notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) <doi:10.1038/s44387-025-00033-7>.

This package provides functions to estimate the optimal threshold of diagnostic markers or treatment selection markers. The optimal threshold is the marker value that maximizes the utility of the marker based-strategy (for diagnostic or treatment selection) in a given population. The utility function depends on the type of marker (diagnostic or treatment selection), but always takes into account the preferences of the patients or the physician in the decision process. For estimating the optimal threshold, ones must specify the distributions of the marker in different groups (defined according to the type of marker, diagnostic or treatment selection) and provides data to estimate the parameters of these distributions. Ones must also provide some features of the target populations (disease prevalence or treatment efficacies) as well as the preferences of patients or physicians. The functions rely on Bayesian inference which helps producing several indicators derived from the optimal threshold. See Blangero, Y, Rabilloud, M, Ecochard, R, and Subtil, F (2019) <doi:10.1177/0962280218821394> for the original article that describes the estimation method for treatment selection markers and Subtil, F, and Rabilloud, M (2019) <doi:10.1002/bimj.200900242> for diagnostic markers.

This package provides a toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del à ndice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella.