Creation and selection of (Advanced) Coupled Matrix and Tensor Factorization (ACMTF) and ACMTF-Regression (ACMTF-R) models. Selection of the optimal number of components can be done using ACMTF_modelSelection() and ACMTFR_modelSelection()'. The CMTF and ACMTF methods were originally described by Acar et al., 2011 <doi:10.48550/arXiv.1105.3422> and Acar et al., 2014 <doi:10.1186/1471-2105-15-239>, respectively.

Implement tableGrob object as a clickable image map. The clickableImageMap package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a tableGrob', all of these features are controllable. In particular, the ggplot2 grid system allows exact positioning of the table relative to other graphics etc.

Extends the functionality of base R lists and provides specialized data structures deque', set', dict', and dict.table', the latter to extend the data.table package.

Proposed by Harrell, the C index or concordance C, is considered an overall measure of discrimination in survival analysis between a survival outcome that is possibly right censored and a predictive-score variable, which can represent a measured biomarker or a composite-score output from an algorithm that combines multiple biomarkers. This package aims to statistically compare two C indices with right-censored survival outcome, which commonly arise from a paired design and thus resulting two correlated C indices.

This package provides a comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, with a mix of tabular and spatial data.

This package provides easy access to historical climate data in Canada from R. Search for weather stations and download raw hourly, daily or monthly weather data across Canada from 1840 to present. Implements public API access as detailed at <https://climate.weather.gc.ca>.

Data analysis often requires coding, especially when data are collected through interviews, observations, or questionnaires. As a result, code counting and data preparation are essential steps in the analysis process. Analysts may need to count the codes in a text (Tokenization, counting of pre-established codes, computing the co-occurrence matrix by line) and prepare the data (e.g., min-max normalization, Z-score, robust scaling, Box-Cox transformation, and non-parametric bootstrap). For the Box-Cox transformation (Box & Cox, 1964, <https://www.jstor.org/stable/2984418>), the optimal Lambda is determined using the log-likelihood method. Non-parametric bootstrap involves randomly sampling data with replacement. Two random number generators are also integrated: a Lehmer congruential generator for uniform distribution and a Box-Muller generator for normal distribution. Package for educational purposes.

Evaluate arbitrary function calls using workers on HPC schedulers in single line of code. All processing is done on the network without accessing the file system. Remote schedulers are supported via SSH.

Accuracy metrics are commonly used to assess the discriminating ability of diagnostic tests or biomarkers. Among them, metrics based on the ROC framework are particularly popular. When classification involves subclasses, the package CompClassMetrics includes functions that can provide the point estimate, confidence interval as well as true values if a parametric setting is known. For more details see Nan and Tian (2025) <doi:10.1177/09622802251343600>, Nan and Tian (2023) <doi:10.1002/sim.9908>, Feng and Tian (2020) <doi:10.1177/0962280220938077> and Wang et al (2016) <doi:10.1002/sim.6843>.

Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms.

This package implements lasso and ridge regression for dichotomised outcomes (<doi:10.1080/02664763.2023.2233057>), i.e., numerical outcomes that were transformed to binary outcomes. Such artificial binary outcomes indicate whether an underlying measurement is greater than a threshold.

This package provides a collection of easy-to-use functions for creating visualizations of compositional data using ggplot2'. Includes support for common plotting techniques in compositional data analysis.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

Wraps the CIRCE (<https://github.com/ohdsi/circe-be>) Java library allowing cohort definition expressions to be edited and converted to Markdown or SQL'.

Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/vertex-ai>, which provides cloud tools for training machine learning models.

This package implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

This package implements cross-validation methods for linear and ridge regression models. The package provides grid-based selection of the ridge penalty parameter using Singular Value Decomposition (SVD) and supports K-fold cross-validation, Leave-One-Out Cross-Validation (LOOCV), and Generalized Cross-Validation (GCV). Computations are implemented in C++ via RcppArmadillo with optional parallelization using RcppParallel'. The methods are suitable for high-dimensional settings where the number of predictors exceeds the number of observations.

This package provides methods for difference-in-differences with a continuous treatment and staggered treatment adoption. Includes estimation of treatment effects and causal responses as a function of the dose, event studies indexed by length of exposure to the treatment, and aggregation into overall average effects. Uniform inference procedures are included, along with both parametric and nonparametric models for treatment effects. The methods are based on Callaway, Goodman-Bacon, and Sant'Anna (2025) <doi:10.48550/arXiv.2107.02637>.

This package provides R routine for the so called two-sample Cramer-Test. This nonparametric two-sample-test on equality of the underlying distributions can be applied to multivariate data as well as univariate data. It offers two possibilities to approximate the critical value both of which are included in this package.

CIFTI files contain brain imaging data in "grayordinates," which represent the gray matter as cortical surface vertices (left and right) and subcortical voxels (cerebellum, basal ganglia, and other deep gray matter). ciftiTools provides a unified environment for reading, writing, visualizing and manipulating CIFTI-format data. It supports the "dscalar," "dlabel," and "dtseries" intents. Grayordinate data is read in as a "xifti" object, which is structured for convenient access to the data and metadata, and includes support for surface geometry files to enable spatially-dependent functionality such as static or interactive visualizations and smoothing.

This package provides a user friendly function crrcbcv to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.

This package provides functionality for the analysis of clustered data using the cluster bootstrap.

This package provides a series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) <DOI:10.1186/s12898-019-0246-8>. The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations.