This package provides functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression.

Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design. Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate. It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context.

Estimate False Discovery Rates (FDRs) for importance metrics from random forest runs.

This repository contains the codes for using the predictive accuracy comparison tests developed in Pitarakis, J. (2023) <doi:10.1017/S0266466623000154>.

This package implements the Phylogeny-Guided Microbiome OTU-Specific Association Test method, which boosts the testing power by adaptively borrowing information from phylogenetically close OTUs (operational taxonomic units) of the target OTU. This method is built on a kernel machine regression framework and allows for flexible modeling of complex microbiome effects, adjustments for covariates, and can accommodate both continuous and binary outcomes.

Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are DBI connections.

Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided.

This package provides a set of tools to implement the non-parametric bounds and Bayesian methods for assessing post-treatment bias developed in Blackwell, Brown, Hill, Imai, and Yamamoto (2025) <doi:10.1017/pan.2025.3>.

Generate nicely formatted HTML tables to display estimation results for pharmacometric models.

This package provides functions to assist in diagnostics and plotting during the causal inference modeling process. Supplements the bartCause package.

This package provides a power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.

The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).

Aims at detecting single nucleotide variation (SNV) and insertion/deletion (INDEL) in circulating tumor DNA (ctDNA), used as a surrogate marker for tumor, at each base position of an Next Generation Sequencing (NGS) analysis. Mutations are assessed by comparing the minor-allele frequency at each position to the measured PER in control samples.

Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).

This package provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.

There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) <doi:10.1017/S0305004100027419>. The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results.

Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons.

Smoothing splines with penalties on order m derivatives.

Control Philips Hue smart lighting. Use this package to connect to a Hue bridge on your local network (remote authentication not yet supported) and control your smart lights through the Philips Hue API. All API V1 endpoints are supported. See API documentation at <https://developers.meethue.com/>.

Access the Public Transport Victoria Timetable API <https://www.ptv.vic.gov.au/footer/data-and-reporting/datasets/ptv-timetable-api/>, with results returned as familiar R data structures. Retrieve information on stops, routes, disruptions, departures, and more.

Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test.

Implementation of the pattern recognition technique Principal Component Pursuit tailored to environmental health data, as described in Gibson et al (2022) <doi:10.1289/EHP10479>.

This package provides functions to visualise sports data. Converts data into a format suitable for plotting charts. Helps to ease the process of working with messy sports data to a more user friendly format. Football data is accessed through worldfootballR <https://github.com/JaseZiv/worldfootballR> which gets data from FBref <https://fbref.com/en>, Transfermarkt <https://www.transfermarkt.com/>, Understat <https://understat.com/>, and fotmob <https://www.fotmob.com/>.

Many datasets and a set of graphics (based on ggplot2), statistics, effect sizes and hypothesis tests are provided for analysing paired data with S4 class.