This package implements seven estimators for average treatment effect (ATE) estimation in mixed-subjects designs (MSDs), where human subjects data is augmented with predictions from large language models (LLMs). Includes Difference-in-Means, GREG, PPI++, Doubly-Tuned, Difference-in-Predictions (DiP), DiP++, and D-T DiP estimators. Provides point estimates, variance estimation via delta-method or bootstrap, and optimal design selection for budget allocation between human observations and LLM predictions.

This package provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.

Software to aid in modeling and analyzing mass-spectrometry-based proteome melting data. Quantitative data is imported and normalized and thermal behavior is modeled at the protein level. Methods exist for normalization, modeling, visualization, and export of results. For a general introduction to MS-based thermal profiling, see Savitski et al. (2014) <doi:10.1126/science.1255784>.

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

Analyse and visualise multi electrode array data at the single electrode and whole well level, downstream of AxIS Navigator 3.6.2 Software processing. Compare bursting parameters between time intervals and recordings using the bar chart visualisation functions. Compatible with 12- and 24- well plates.

Evaluate hypotheses concerning the distribution of multinomial proportions using bridge sampling. The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.

This package implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

Relatively easy access is provided to 2023 version of the Maddison project data downloaded 2025-08-28. This project collates all the credible data on population and GDP for 169 countries, with some dating back to the year 1 of the current era. One function makes it easy to find the leaders for each year, allowing users to delete countries like OPEC with narrow economies to focus on technology leaders. Another function makes it easy to plot data for only selected countries or years. Another function makes it relatively easy to obtain references to the original sources, which must be cited per the copyright rules of the Maddison Project for different uses of their data.

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

This package performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.

Automate the explanatory analysis of machine learning predictive models. Generate advanced interactive model explanations in the form of a serverless HTML site with only one line of code. This tool is model-agnostic, therefore compatible with most of the black-box predictive models and frameworks. The main function computes various (instance and model-level) explanations and produces a customisable dashboard, which consists of multiple panels for plots with their short descriptions. It is possible to easily save the dashboard and share it with others. modelStudio facilitates the process of Interactive Explanatory Model Analysis introduced in Baniecki et al. (2023) <doi:10.1007/s10618-023-00924-w>.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/data-research>.

This package provides tools for high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn (2014) <doi:10.1093/biomet/ast042> and Genz and Bretz (2009) <doi:10.1007/978-3-642-01689-9>.

There are an increasing number of mega-phylogenies available nowadays, with many of them being sets of thousands of posterior distribution phylogenies. For ecological studies, we may need to randomly select many such posterior phylogenies to conduct analyses. This data package serves this purpose by providing a small number (100 or 50) of randomly selected posterior phylogenies (if available) so that we can readily use them for our downstream analyses without repeating the downloading and selecting processes.

Tests of comparison of two or more survival curves. Allows for comparison of more than two survival curves whether the proportional hazards hypothesis is verified or not.

Simulates Blockchain-Enabled Maritime Transportation Systems (BMTS) by integrating asynchronous, great-circle vessel kinematics with decentralised edge-computing consensus layers. The package provides a fully self-contained cyber-range running completely offline, permitting the risk-free injection of spatial and telemetry threat vectors (e.g., geofence breaches, coordinate modification, and node impersonation). It includes an interactive workspace engine built using shinydashboard and leaflet map layers for monitoring vessel states and ledger updates. Implemented consensus structures are modeled on a lightweight Proof-of-Authentication framework optimized for resource-constrained distributed nodes.

This package provides comorbidity classification algorithms such as the Pediatric Complex Chronic Conditions (PCCC), Charlson, and Elixhauser indices, supports longitudinal comorbidity flagging across encounters, and includes utilities for working with medical coding schemas such as the International Classification of Diseases (ICD).

Create legends for maps and other graphics. Thematic maps need to be accompanied by legible legends to be fully comprehensible. This package offers a wide range of legends useful for cartography, some of which may also be useful for other types of graphics.

This package provides a step-based pipeline for reproducible survey data processing, building on the survey package for complex sampling designs. Supports rotating panels with bootstrap replicate weights, and provides a recipe system for sharing and reproducing data transformation workflows across survey editions.

This package provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies.

Calculate predicted levels and marginal effects, using the delta method to calculate standard errors. This is an R-based version of the margins command from Stata.

This package provides tools and demonstrates methods for working with individual undergraduate student-level records (registrar's data) in R'. Tools include filters for program codes, data sufficiency, and timely completion. Methods include gathering blocs of records, computing quantitative metrics such as graduation rate, and creating charts to visualize comparisons. midfieldr interacts with practice data provided in midfielddata', an R data package available at <https://midfieldr.github.io/midfielddata/>. midfieldr also interacts with the full MIDFIELD database for users who have access. As of the transfer of MIDFIELD to the American Society for Engineering Education in 2023, the development, expansion, and study of MIDFIELD has been supported by the National Science Foundation grants 0337629, 0646441, 0729596, 0734062, 0835914, 0935157, 0935058, 0969474, 1025171, 1129383, 1232740, 1329283, 1361058, 1545667, 2142087, 2141903, and 2152441.

This package provides programmatic access to the Meetup GraphQL API (<https://www.meetup.com/graphql/>), enabling users to retrieve information about groups, events, and members from Meetup (<https://www.meetup.com/>). Supports authentication via OAuth2 and includes functions for common queries and data manipulation tasks.

This package provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data. Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.