This package contains implementations of the integrative Cox model with uncertain event times proposed by Wang, et al. (2020) <doi:10.1214/19-AOAS1287>, the regularized Cox cure rate model with uncertain event status proposed by Wang, et al. (2023) <doi:10.1007/s12561-023-09374-w>, and other survival analysis routines including the Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, the regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>.

Converts matrices and lists of matrices into a single vector by interleaving their values. That is, each element of the result vector is filled from the input matrices one row at a time. This is the same as transposing a matrix, then removing the dimension attribute, but is designed to operate on matrices in nested list structures.

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning.

Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.

Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.

This package provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/> that powers the American Soccer Analysis app.

Plots the conditional coefficients ("marginal effects") of variables included in multiplicative interaction terms.

This package provides a shiny app that supports both dual and bulk RNA-seq, with the dual RNA-seq functionality offering the flexibility to perform either a sequential approach (where reads are mapped separately to each genome) or a combined approach (where reads are aligned to a single merged genome). The user-friendly interface automates the analysis process, providing step-by-step guidance, making it easy for users to navigate between different analysis steps, and download intermediate results and publication-ready plots.

Synthesize images into characteristic features for time-series analysis or machine learning applications. The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

Support for implicit expansion of arrays in operations involving arrays of mismatching sizes. This pattern is known as "broadcasting" in Python and "implicit expansion" in Matlab and is explained for example in the article "Array programming with NumPy" by C. R. Harris et al. (2020) <doi:10.1038/s41586-020-2649-2>.

This package implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.

This package provides a suite of functions to use with regression models, including summaries, residual plots, and factor comparisons. Used as part of the Model Fitting module of iNZight', a graphical user interface providing easy exploration and visualisation of data for students of statistics, available in both desktop and online versions.

This package provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.

An R client for the iplookupapi.com IP Lookup API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://iplookupapi.com/docs> .

This package provides tools for manipulating, visualizing, and exporting raster images in R. Designed as an educational resource for students learning the basics of remote sensing, the package provides user-friendly functions to apply color ramps, export RGB composites, and create multi-frame visualizations. Built on top of the terra and ggplot2 packages. See <https://github.com/ducciorocchini/imageRy> for more details and examples.

This package provides a suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. â ideanetâ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, â ideanetâ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

This package implements the Information Matrix test for regression models following Cameron, A. C., & Trivedi, P. K. (1990) <https://cameron.econ.ucdavis.edu/research/imtest_impliedalternatives_ucdwp372.pdf> Decomposes the test into components for heteroscedasticity, skewness, and kurtosis to diagnose specific forms of misspecification. Provides both overall and component-wise statistics for model assessment.

ISA is a metadata framework to manage an increasingly diverse set of life science, environmental and biomedical experiments. In isatabr methods for reading, modifying and writing of files in the ISA-Tab format are implemented. It also contains methods for processing assay data.

This package implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data.

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

Iterator for generating permutations and combinations. They can be either drawn with or without replacement, or with distinct/ non-distinct items (multiset). The generated sequences are in lexicographical order (dictionary order). The algorithms to generate permutations and combinations are memory efficient. These iterative algorithms enable users to process all sequences without putting all results in the memory at the same time. The algorithms are written in C/C++ for faster performance. Note: iterpc is no longer being maintained. Users are recommended to switch to arrangements'.