Streamlines the setup and execution of network studies using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). Creates standardised project structures with template code, manages dependencies with renv', provides code review utilities, and supports containerised execution with Docker for reproducible multi-site studies. Includes GitHub integration for collaboration and version control.

This package provides a growing collection of personal utility functions. Currently provides tools to parse Rich Text Format (RTF) files and extract their tables into data frames, automatically detecting header rows, merging multi-page tables, and resolving merged cells. Particularly useful for tables produced by SAS or by the r2rtf package, which are commonly used for clinical trial and regulatory reporting.

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.

We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length.

Several Oceanographic data sets are provided for use by the oce package and for other purposes.

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.

This package provides a collection of functions to construct sets of orthogonal polynomials and their recurrence relations. Additional functions are provided to calculate the derivative, integral, value and roots of lists of polynomial objects.

Reconstructs plausible 2 by 2 contingency tables from published cohort-study summaries when the original cell counts are unavailable. Given group sample sizes and an odds ratio with partial confidence interval information, the package searches for compatible event counts, then derives corresponding relative risks and confidence intervals. It implements the methods described in Wang (2013) <doi:10.18637/jss.v055.i05> and includes summary and plotting methods for reviewing admissible scenarios.

Over 30 years of daily commit activity and contributor growth for FreeBSD', OpenBSD', NetBSD', and PostgreSQL'. Built from cloned git repositories for complete coverage -- not limited by API quotas. Includes daily commits, daily new committers, weekly aggregates, and Phabricator sign-up data. Designed for time series analysis, growth modeling, and cross-project comparison. Contains no personal data.

Search and import data directly to R from the Spanish Sociological Research Center (CIS) <https://www.cis.es/inicio>. The CIS is a public institution that conducts electoral and sociological research studies on the Spanish society. The CIS has a large database of surveys that can be accessed through its website. The package includes functions to search for surveys, survey questions and timeseries, and import the data directly to R.

Simultaneously evaluate multiple ordinal outcome measures. Applied data analysts in particular are faced with uncertainty in choosing appropriate statistical tests for ordinal data. The included shiny application allows users to simulate outcomes given different ordinal data distributions.

This package provides a generalised data structure for fast and efficient loading and data munching of sparse omics data. The OmicFlow requires an up-front validated metadata template from the user, which serves as a guide to connect all the pieces together by aligning them into a single object that is defined as an omics class. Once this unified structure is established, users can perform manual subsetting, visualisation, and statistical analysis, or leverage the automated autoFlow method to generate a comprehensive report.

Read, validate, and load Open Knowledge Format (OKF) bundles (a directory of markdown files with YAML frontmatter) into a portable DuckDB catalog, build the concept graph, render to HTML, and optionally embed concept bodies for semantic search. Deterministic and agent-free: the same bundle always yields the same catalog, graph, and render, with no LLM calls in the core. Conformant and permissive per the OKF v0.1 specification.

Solves linear systems of form Ax=b via Gauss elimination, LU decomposition, Gauss-Seidel, Conjugate Gradient Method (CGM) and Cholesky methods.

Represents the basis functions for B-splines in a simple matrix formulation that facilitates, taking integrals, derivatives, and making orthogonal the basis functions.

An RStudio addin to assist with removing objects from the global environment. Features include removing objects according to name patterns and object type. During the course of an analysis, temporary objects are often created and this tool assists with removing them quickly. This can be useful when memory management within R is important.

It implements the online Bayesian methods for change point analysis. It can also perform missing data imputation with methods from VIM'. The reference is Yigiter A, Chen J, An L, Danacioglu N (2015) <doi:10.1080/02664763.2014.1001330>. The link to the package is <https://CRAN.R-project.org/package=onlineBcp>.

Calculate the optimal sample size allocation that uses the minimum resources to achieve targeted statistical power in experiments. Perform power analyses with and without accommodating costs and budget. The designs cover single-level and multilevel experiments detecting main, mediation, and moderation effects (and some combinations). The references for the proposed methods include: (1) Shen, Z., & Kelcey, B. (2020). Optimal sample allocation under unequal costs in cluster-randomized trials. Journal of Educational and Behavioral Statistics, 45(4): 446-474. <doi:10.3102/1076998620912418>. (2) Shen, Z., & Kelcey, B. (2022b). Optimal sample allocation for three-level multisite cluster-randomized trials. Journal of Research on Educational Effectiveness, 15 (1), 130-150. <doi:10.1080/19345747.2021.1953200>. (3) Shen, Z., & Kelcey, B. (2022a). Optimal sample allocation in multisite randomized trials. The Journal of Experimental Education, 90(3), 693-711. <doi:10.1080/00220973.2020.1830361>. (4) Shen, Z., Leite, W., Zhang, H., Quan, J., & Kuang, H. (2025). Using ant colony optimization to identify optimal sample allocations in cluster-randomized trials. The Journal of Experimental Education, 93(1), 167-185. <doi:10.1080/00220973.2024.2306392>. (5) Shen, Z., Li, W., & Leite, W. (in press). Statistical power and optimal design for randomized controlled trials investigating mediation effects. Psychological Methods. <doi:10.1037/met0000698>. (6) Champely, S. (2020). pwr: Basic functions for power analysis (Version 1.3-0) [Software]. Available from <https://CRAN.R-project.org/package=pwr>.

This package provides a Shiny application for exploring and visualizing Olympic Games data from 1896 onwards, including both Summer and Winter Olympic Games. The application provides interactive visualizations of athletes, countries, sports, events, and medal results. Olympic data are obtained from the olympicAthletes R package.

Access data from the "City of Toronto Open Data Portal" (<https://open.toronto.ca>) directly from R.

This package provides functionality to construct standardised tables from health care data formatted according to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. The package includes tools to build key tables such as observation period and drug era, among others.

This package provides access to selected functions and data available through any OpenBioMaps <https://openbiomaps.org> server instance. OpenBioMaps is an open-source biodiversity data management platform designed for conservation professionals and researchers. User authentication and access control are handled through OpenBioMaps login credentials.

This package provides clustering of genes with similar dose response (or time course) profiles. It implements the method described by Lin et al. (2012).

This package provides functions for quickly creating R and Python scripts, as well as Rmarkdown or Quarto documents with automatically assigned name prefixes. Prefixes are either file counts (e.g. "001") or dates (e.g. "2022-09-26").