SingleCellMultiModal is an ExperimentHub package that serves multiple datasets obtained from GEO and other sources and represents them as MultiAssayExperiment objects. We provide several multi-modal datasets including scNMT, 10X Multiome, seqFISH, CITEseq, SCoPE2, and others. The scope of the package is is to provide data for benchmarking and analysis. To cite, use the citation function and see <https://doi.org/10.1371/journal.pcbi.1011324>.

The runtime is the executable part of every AppImage. It mounts the payload via FUSE and executes the entrypoint, allowing users to run applications in a portable manner without the need for installation. This runtime ensures that the AppImage can access its bundled libraries and resources seamlessly, providing a consistent environment across different Linux distributions. In the absence of FUSE, the AppImage can still be started using the --appimage-extract-and-run flag.

This package provides a tm Source to create corpora from articles exported from the LexisNexis content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages). Note that the file format is highly unstable: there is no warranty that this package will work for your corpus, and you may have to adjust the code to adapt it to your particular format.

This package contains a WGS84 datum map of the USA, which includes all Commonwealth and State boundaries & also includes Puerto Rico and the U.S. Virgin Islands. This map is a reprojection of the NAD83 datum map from the USGS National Map. This package contains a subset of the data included in the USA.state.boundaries.data package, which is available in a drat repository. To install that data package, please follow the instructions at <https://gitlab.com/iembry/usa.state.boundaries.data>.

This package provides a user-friendly shiny application to explore statistical associations and visual patterns in multivariate datasets. The app provides interactive correlation networks, bivariate plots, and summary tables for different types of variables (numeric and categorical). It also supports optional survey weights and range-based filters on association strengths, making it suitable for the exploration of survey and public data by non-technical users, journalists, educators, and researchers. For background and methodological details, see Soetewey et al. (2025) <doi:10.1016/j.softx.2025.102483>.

This package provides estimates of several summary measures for clinical trials including the average hazard ratio, the weighted average hazard ratio, the restricted superiority probability ratio, the restricted mean survival difference and the ratio of restricted mean times lost, based on the short-term and long-term hazard ratio model (Yang, 2005 <doi:10.1093/biomet/92.1.1>) which accommodates various non-proportional hazards scenarios. The inference procedures and the asymptotic results for the summary measures are discussed in Yang (2018, <doi:10.1002/sim.7676>).

This package provides methods for analyzing the dispersion of tabular datasets with batched and ordered samples. Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and Salanon (2025) <doi:10.1101/2025.08.01.668073>.

Runs multiple individual time series models, and combines them into an ensembles of time series models. This is mainly used to predict the results of the monthly labor market report from the United States Bureau of Labor Statistics for virtually any part of the economy reported by the Bureau of Labor Statistics, but it can be easily modified to work with other types of time series data. For example, the package was used to predict the winning men's and women's time for the 2024 London Marathon.

Functionality for segmenting individual trees from a forest stand scanned with a close-range (e.g., terrestrial or mobile) laser scanner. The complete workflow from a raw point cloud to a complete tabular forest inventory is provided. The package contains several algorithms for detecting tree bases and a graph-based algorithm to attach all remaining points to these tree bases. It builds heavily on the lidR package. A description of the segmentation algorithm can be found in Larysch et al. (2025) <doi:10.1007/s10342-025-01796-z>.

Tests for a comparison of two partially overlapping samples. A comparison of means using the partially overlapping samples t-test: See Derrick, Russ, Toher and White (2017), Test statistics for the comparison of means for two samples which include both paired observations and independent observations, Journal of Modern Applied Statistical Methods, 16(1). A comparison of proportions using the partially overlapping samples z-test: See Derrick, Dobson-Mckittrick, Toher and White (2015), Test statistics for comparing two proportions with partially overlapping samples. Journal of Applied Quantitative Methods, 10(3).

This package contains an implementation of StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.

If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another.

Providing the kubernetes-like class ManagedCloudProvider as a child class of the CloudProvider class in the DockerParallel package. The class is able to manage the cloud instance made by the non-kubernetes cloud service. For creating a provider for the non-kubernetes cloud service, the developer needs to define a reference class inherited from ManagedCloudProvider and define the method for the generics runDockerWorkerContainers(), getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4).

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.

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.

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

This package implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a hybrid control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the SAMprior package (v1.1.1) by Yang et al. (2023) <https://academic.oup.com/biometrics/article/79/4/2857/7587575> to support the Self-Adapting Mixture (SAM) prior framework for comparison.

The Food and Agriculture Organization-56 Penman-Monteith is one of the important method for estimating evapotranspiration from vegetated land areas. This package helps to calculate reference evapotranspiration using the weather variables collected from weather station. Evapotranspiration is the process of water transfer from the land surface to the atmosphere through evaporation from soil and other surfaces and transpiration from plants. The package aims to support agricultural, hydrological, and environmental research by offering accurate and accessible reference evapotranspiration calculation. This package has been developed using concept of Córdova et al. (2015)<doi:10.1016/j.apm.2022.09.004> and Debnath et al. (2015) <doi:10.1007/s40710-015-0107-1>.

This package implements the Age Band Decomposition (ABD) method for standardizing tree ring width data while preserving both low and high frequency variability. Unlike traditional detrending approaches that can distort long term growth trends, ABD decomposes ring width series into multiple age classes, detrends each class separately, and then recombines them to create standardized chronologies. This approach improves the detection of growth signals linked to past climatic and environmental factors, making it particularly valuable for dendroecological and dendroclimatological studies. The package provides functions to perform ABD-based standardization, compare results with other common methods (e.g., BAI, C method, RCS), and facilitate the interpretation of growth patterns under current and future climate variability.

This package provides a study based on the screened selection design (SSD) is an exploratory phase II randomized trial with two or more arms but without concurrent control. The primary aim of the SSD trial is to pick a desirable treatment arm (e.g., in terms of the response rate) to recommend to the subsequent randomized phase IIb (with the concurrent control) or phase III. The proposed designs can â partiallyâ control or provide the empirical type I error/false positive rate by an optimal algorithm (implemented by the optimal_2arm_binary() or optimal_3arm_binary() function) for each arm. All the design needed components (sample size, operating characteristics) are supported.

We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.

Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer. Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>. The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".

This package provides functions to load and analyze three open Electronic Health Records (EHRs) datasets of patients diagnosed with glioblastoma, previously released under the Creative Common Attribution 4.0 International (CC BY 4.0) license. Users can generate basic descriptive statistics, frequency tables and save descriptive summary tables, as well as create and export univariate or bivariate plots. The package is designed to work with the included datasets and to facilitate quick exploratory data analysis and reporting. More information about these three datasets of EHRs of patients with glioblastoma can be found in this article: Gabriel Cerono, Ombretta Melaiu, and Davide Chicco, Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme', Journal of Healthcare Informatics Research 8, 1-18 (March 2024). <doi:10.1007/s41666-023-00138-1>.