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.

Use health data in the Observational Medical Outcomes Partnership Common Data Model format in Spark'. Functionality includes creating all required tables and fields and creation of a single reference to the data. Native Spark functionality is supported.

This package provides tools for managing nested, multi-level configuration systems with runtime mutability, type validation, and default value management. Supports creating hierarchical options managers with customizable validators for scalar and vector types (numeric, character, logical), enumerated values, bounded ranges, and complex structures like XY pairs. Options can be dynamically modified at runtime while maintaining type safety through validator functions, and easily reset to their default values when needed.

This package provides a method for the quantitative prediction using omics data. This package provides functions to construct the quantitative prediction model using omics data.

This package creates a client with queries for the UK Open Banking ('Open Data') API.

This package provides a client for the open-source monitoring and alerting toolkit, Prometheus', that emits metrics in the OpenMetrics format. Allows users to automatically instrument Plumber and Shiny applications, collect standard process metrics, as well as define custom counter, gauge, and histogram metrics of their own.

Perform a Bayesian estimation of the ordinal exploratory Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data described by Culpepper, S. A. and Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>.

An interface for interacting with OSF (<https://osf.io>). osfr enables you to access open research materials and data, or create and manage your own private or public projects.

Calculate the ratio of iron oxides, hematite and goethite, in soil using the diffuse reflectance technique. The Kubelka-Munk theory, second derivative analysis, and spectral region amplitudes related to hematite and goethite content are used for quantification (Torrent, J., & Barron, V. (2008) <doi:10.2136/sssabookser5.5.c13>). Additionally, the package calculates soil color in the visible spectrum using Munsell and RGB color spaces, based on color theory (Viscarra et al. (2006) <doi:10.1016/j.geoderma.2005.07.017>).

This package provides a framework for organizing R projects with a standardized structure. Most analyses consist of three main components: code, results, and data, each with different requirements such as version control, sharing, and encryption. This package provides tools to set up and manage project directories, handle file paths consistently across operating systems, organize results using date-based structures, source code from specified directories, and perform file operations safely. It ensures consistency across projects while accommodating different requirements for various types of content.

Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with a pre-specified correlation matrix and marginal distributions. The details of the method are explained in Demirtas et al. (2015) <DOI:10.1080/10543406.2014.920868>.

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 analyse, interpret and understand noise pollution data. Data are typically regular time series measured with sound meter. The package is partially described in Fogola, Grasso, Masera and Scordino (2023, <DOI:10.61782/fa.2023.0063>).

Calculate similarity between ontological terms and sets of ontological terms based on term information content and assess statistical significance of similarity in the context of a collection of terms sets - Greene et al. 2017 <doi:10.1093/bioinformatics/btw763>.

This package contains data from the May 2020 Occupational Employment and Wage Statistics data release from the U.S. Bureau of Labor Statistics. The dataset covers employment and wages across occupations, industries, states, and at the national level. Metropolitan data is not included.

This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.

Intended to assist in the choice of the sampling strategy to implement in a survey.

This package implements ordered beta regression models, which are for modeling continuous variables with upper and lower bounds, such as survey sliders, dose-response relationships and indexes. For more information, see Kubinec (2023) <doi:10.31235/osf.io/2sx6y>. The package is a front-end to the R package brms', which facilitates a range of regression specifications, including hierarchical, dynamic and multivariate modeling.

Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>. The degree of balance can be specified for each covariate. In addition, sampling weights can be estimated that allow a sample to generalize to a population specified with given target moments of covariates, as in matching-adjusted indirect comparison (MAIC).

An implementation of the Blinder-Oaxaca decomposition for linear regression models.

This package provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the mlr package. Supported random forest packages are randomForest and ranger and trained models of these packages with the train function of mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for mtry', sample.fraction and min.node.size for the ranger package.

This package provides a database management tool built as a shiny application. Connect to various databases to send queries, upload files, preview tables, and more.

Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.

This package provides a framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "onlineforecast: An R package for adaptive and recursive forecasting" <https://journal.r-project.org/articles/RJ-2023-031/>.