Extract results into R from the Observational Health Data Sciences and Informatics result database (see <https://ohdsi.github.io/Strategus/results-schema/index.html>) and generate reports/presentations via quarto that summarize results in HTML format. Learn more about OhdsiReportGenerator at <https://ohdsi.github.io/OhdsiReportGenerator/>.

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 construct confidence intervals for the Overlap Coefficient (OVL). OVL measures the similarity between two distributions through the overlapping area of their distribution functions. Given its intuitive description and ease of visual representation by the straightforward depiction of the amount of overlap between the two corresponding histograms based on samples of measurements from each one of the two distributions, the development of accurate methods for confidence interval construction can be useful for applied researchers. Implements methods based on the work of Franco-Pereira, A.M., Nakas, C.T., Reiser, B., and Pardo, M.C. (2021) <doi:10.1177/09622802211046386>.

This package provides an Interface to Open Collaboration Services OCS (<https://www.open-collaboration-services.org/>) REST API.

Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. omicsTools is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality controlâ based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.

This package provides a regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent non-linearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function) connects the scale with an underlying continuous latent variable. The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. A variety of link functions are currently implemented. Such models are described in Manuguerra et al (2020) <doi:10.18637/jss.v096.i08>.

Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.

This package implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.

This package provides a novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization. To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri- Factorization" which will be submitted to BBRC.

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.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

The olr function systematically evaluates multiple linear regression models by exhaustively fitting all possible combinations of independent variables against the specified dependent variable. It selects the model that yields the highest adjusted R-squared (by default) or R-squared, depending on user preference. In model evaluation, both R-squared and adjusted R-squared are key metrics: R-squared measures the proportion of variance explained but tends to increase with the addition of predictorsâ regardless of relevanceâ potentially leading to overfitting. Adjusted R-squared compensates for this by penalizing model complexity, providing a more balanced view of fit quality. The goal of olr is to identify the most suitable model that captures the underlying structure of the data while avoiding unnecessary complexity. By comparing both metrics, it offers a robust evaluation framework that balances predictive power with model parsimony. Example Analogy: Imagine a gardener trying to understand what influences plant growth (the dependent variable). They might consider variables like sunlight, watering frequency, soil type, and nutrients (independent variables). Instead of manually guessing which combination works best, the olr function automatically tests every possible combination of predictors and identifies the most effective modelâ based on either the highest R-squared or adjusted R-squared value. This saves the user from trial-and-error modeling and highlights only the most meaningful variables for explaining the outcome. A Python version is also available at <https://pypi.org/project/olr>.

This package provides routines for finding an Optimal System of Distinct Representatives (OSDR), as defined by D.Gale (1968) <doi:10.1016/S0021-9800(68)80039-0>.

Extends flexclust with an R implementation of order constrained solutions in k-means clustering (Steinley and Hubert, 2008, <doi:10.1007/s11336-008-9058-z>).

Utilizes the Black-Scholes-Merton option pricing model to calculate key option analytics and perform graphical analysis of various option strategies. Provides functions to calculate the option premium and option greeks of European-style options.

Match, download, convert and import Open Street Map data extracts obtained from several providers.

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.

Download and import of OpenStreetMap ('OSM') data as sf or sp objects. OSM data are extracted from the Overpass web server (<https://overpass-api.de/>) and processed with very fast C++ routines for return to R'.

The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

This package provides functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.

Many treatment effect estimators can be written as weighted outcomes. These weights have established use cases like checking covariate balancing via packages like cobalt'. This package takes the original estimator objects and outputs these outcome weights. It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>. This version is compatible with the grf package and provides an internal implementation of Double Machine Learning.

Offers a streamlined programmatic interface to Ordnance Survey's British National Grid (BNG) index system, enabling efficient spatial indexing and analysis based on grid references. It supports a range of geospatial applications, including statistical aggregation, data visualisation, and interoperability across datasets. Designed for developers and analysts working with geospatial data in Great Britain, osbng simplifies integration with geospatial workflows and provides intuitive tools for exploring the structure and logic of the BNG system.

Search and extract data from the Organization for Economic Cooperation and Development (OECD).