This package provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateParadoxTest.html>.

Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.

This package provides a clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. <doi:10.18637/jss.v111.i01>.

Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher-order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.

Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.

PowerMock is a framework that extends other mock libraries such as EasyMock with more powerful capabilities. PowerMock uses a custom classloader and bytecode manipulation to enable mocking of static methods, constructors, final classes and methods, private methods, removal of static initializers and more. By using a custom classloader no changes need to be done to the IDE or continuous integration servers which simplifies adoption.

This is an ExperimentHub Data package that helps to access the spatially-resolved transcriptomics and single-nucleus RNA sequencing data. The datasets are generated from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. The datasets are based on [spatial_hpc](https://github.com/LieberInstitute/spatial_hpc) project by Lieber Institute for Brain Development (LIBD) researchers and collaborators.

Utilize the shiny interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the shiny session. Model diagnostics can be included in a rmarkdown document and rendered to desired output format.

This package provides sample size and power calculations when the treatment time-lag effect is present and the lag duration is either homogeneous across the individual subject, or varies heterogeneously from individual to individual within a certain domain and following a specific pattern. The methods used are described in Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) <doi:10.1002/sim.7157>.

Includes an interactive application designed to support educators in wide-ranging disciplines, with a particular focus on those teaching introductory statistical methods (descriptive and/or inferential) for data analysis. Users are able to randomly generate data, make new versions of existing data through common adjustments (e.g., add random normal noise and perform transformations), and check the suitability of the resulting data for statistical analyses.

The Resources Plugin handles the copying of project resources to the output directory. There are two different kinds of resources: main resources and test resources. The difference is that the main resources are the resources associated to the main source code while the test resources are associated to the test source code.

Thus, this allows the separation of resources for the main source code and its unit tests.

ISAAC (Indirection, Shift, Accumulate, Add, and Count) is a fast pseudo-random number generator. It is suitable for applications where a significant amount of random data needs to be produced quickly, such as solving using the Monte Carlo method or for games. The results are uniformly distributed, unbiased, and unpredictable unless you know the seed.

This package provides a Perl interface to the ISAAC pseudo random number generator.

Historical borrowing in clinical trials can improve precision and operating characteristics. This package supports a longitudinal hierarchical model to borrow historical control data from other studies to better characterize the control response of the current study. It also quantifies the amount of borrowing through longitudinal benchmark models (independent and pooled). The hierarchical model approach to historical borrowing is discussed by Viele et al. (2013) <doi:10.1002/pst.1589>.

Most multilevel methodologies can only model macro-micro multilevel situations in an unbiased way, wherein group-level predictors (e.g., city temperature) are used to predict an individual-level outcome variable (e.g., citizen personality). In contrast, this R package enables researchers to model micro-macro situations, wherein individual-level (micro) predictors (and other group-level predictors) are used to predict a group-level (macro) outcome variable in an unbiased way.

This is a collection of econometric functions for performance and risk analysis. This package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.

MultimodalExperiment is an S4 class that integrates bulk and single-cell experiment data; it is optimally storage-efficient, and its methods are exceptionally fast. It effortlessly represents multimodal data of any nature and features normalized experiment, subject, sample, and cell annotations, which are related to underlying biological experiments through maps. Its coordination methods are opt-in and employ database-like join operations internally to deliver fast and flexible management of multimodal data.

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.

Computes a novel metric of affinity between two entities based on their co-occurrence (using binary presence/absence data). The metric and its MLE, alpha hat, were advanced in Mainali, Slud, et al, 2021 <doi:10.1126/sciadv.abj9204>. Various types of confidence intervals and median interval were developed in Mainali and Slud, 2022 <doi:10.1101/2022.11.01.514801>. The `finches` dataset is now bundled internally (no longer pulled via the cooccur package, which has been dropped).

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 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.