Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.

This package provides tools for implementing covariate-adjusted response-adaptive procedures for binary, continuous and survival responses. Users can flexibly choose between two functions based on their specific needs for each procedure: use real patient data from clinical trials to compute allocation probabilities directly, or use built-in simulation functions to generate synthetic patient data. Detailed methodologies and algorithms used in this package are described in the following references: Zhang, L. X., Hu, F., Cheung, S. H., & Chan, W. S. (2007)<doi:10.1214/009053606000001424> Zhang, L. X. & Hu, F. (2009) <doi:10.1007/s11766-009-0001-6> Hu, J., Zhu, H., & Hu, F. (2015) <doi:10.1080/01621459.2014.903846> Zhao, W., Ma, W., Wang, F., & Hu, F. (2022) <doi:10.1002/pst.2160> Mukherjee, A., Jana, S., & Coad, S. (2024) <doi:10.1177/09622802241287704>.

This package provides a collection of functions to generate a large variety of structures in high dimensions. These data structures are useful for testing, validating, and improving algorithms used in dimensionality reduction, clustering, machine learning, and visualization.

Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).

This package provides interactive command-line menu functionality with single and multiple selection menus, keyboard navigation (arrow keys or vi-style j/k), preselection, and graceful fallback for non-interactive environments. Inspired by tools such as inquirer.js <https://github.com/SBoudrias/Inquirer.js>, pick <https://github.com/aisk/pick>, and survey <https://github.com/AlecAivazis/survey>. Designed to be lightweight and easy to integrate into R packages and scripts.

This package provides a workflow to generate and analyze signatures based on copy number data using non-negative matrix factorization (NMF) in an approach similar to that used in mutational signatures. It can be used to extract features from Copy number segment data and use that to find a subset of copy number signatures which can be further used to correlate with other relevant data. For more on NMF see Gaujoux (2013) <doi:10.1186/1471-2105-11-367>.

Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia <https://github.com/MounaBelaid/covid19datatunisia>.

This package contains the prepared data that is needed for the shiny application examples in the canvasXpress package. This package also includes datasets used for automated testthat tests. Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>. Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.

Canonical correlation analysis (CCA) via reduced-rank regression with support for regularization and cross-validation. Several methods for estimating CCA in high-dimensional settings are implemented. The first set of methods, cca_rrr() (and variants: cca_group_rrr() and cca_graph_rrr()), assumes that one dataset is high-dimensional and the other is low-dimensional, while the second, ecca() (for Efficient CCA) assumes that both datasets are high-dimensional. For both methods, standard l1 regularization as well as group-lasso regularization are available. cca_graph_rrr further supports total variation regularization when there is a known graph structure among the variables of the high-dimensional dataset. In this case, the loadings of the canonical directions of the high-dimensional dataset are assumed to be smooth on the graph. For more details see Donnat and Tuzhilina (2024) <doi:10.48550/arXiv.2405.19539> and Wu, Tuzhilina and Donnat (2025) <doi:10.48550/arXiv.2507.11160>.

Impute the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. CondiS can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, CondiS is extended by incorporating the covariate information through machine learning-based regression modeling ('CondiS_X'), which can further improve the imputed survival time.

Implementation of Tobit type I and type II families for censored regression using the mgcv package, based on methods detailed in Woods (2016) <doi:10.1080/01621459.2016.1180986>.

Use frequentist and Bayesian methods to estimate parameters from a binary outcome misclassification model. These methods correct for the problem of "label switching" by assuming that the sum of outcome sensitivity and specificity is at least 1. A description of the analysis methods is available in Hochstedler and Wells (2023) <doi:10.48550/arXiv.2303.10215>.

Utilities that support the usage of pyDarwin (<https://certara.github.io/pyDarwin/>) for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine.

Maximum likelihood estimation of the Cauchy-Cacoullos (discrete Cauchy) distribution. Probability mass, distribution and quantile function are also included. The reference paper is: Papadatos N. (2022). "The Characteristic Function of the Discrete Cauchy Distribution in Memory of T. Cacoullos". Journal of Statistical Theory Practice, 16(3): 47. <doi:10.1007/s42519-022-00268-6>.

Estimates the per-person conditional standard error of measurement (CSEM) under the persons-by-items single-facet crossed design of Generalizability Theory, following Brennan (1998) <doi:10.1177/014662169802200401>. Implements three estimators of the relative error variance (full, large_a, uncorrelated) and the closed-form absolute error variance, with both analytical and item-resampling bootstrap sampling variances, quadratic smoothing of CSEMs on observed score, D-study extrapolation, and base-graphics plotting.

Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.

Spatio-temporal data from Scotland used in the vignettes accompanying the CARBayes (spatial modelling) and CARBayesST (spatio-temporal modelling) packages. Most of the data relate to the set of 271 Intermediate Zones (IZ) that make up the 2001 definition of the Greater Glasgow and Clyde health board.

When causal quantities are not identifiable from the observed data, it still may be possible to bound these quantities using the observed data. We outline a class of problems for which the derivation of tight bounds is always a linear programming problem and can therefore, at least theoretically, be solved using a symbolic linear optimizer. We extend and generalize the approach of Balke and Pearl (1994) <doi:10.1016/B978-1-55860-332-5.50011-0> and we provide a user friendly graphical interface for setting up such problems via directed acyclic graphs (DAG), which only allow for problems within this class to be depicted. The user can then define linear constraints to further refine their assumptions to meet their specific problem, and then specify a causal query using a text interface. The program converts this user defined DAG, query, and constraints, and returns tight bounds. The bounds can be converted to R functions to evaluate them for specific datasets, and to latex code for publication. The methods and proofs of tightness and validity of the bounds are described in a paper by Sachs, Jonzon, Gabriel, and Sjölander (2022) <doi:10.1080/10618600.2022.2071905>.

Analyzes data from a Conconi et al. (1996) <doi:10.1055/s-2007-972887> treadmill fitness test where speed is augmented by a constant amount every set number of seconds to estimate the anaerobic (lactate) threshold speed and heart rate. It reads a TCX file, allows optional removal observations from before and after the actual test, fits a change-point linear model where the change-point is the estimate of the lactate threshold, and plots the data points and fit model. Details of administering the fitness test are provided in the package vignette. Functions work by default for Garmin Connect TCX exports but may require additional data preparation for heart rate, time, and speed data from other sources.

Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.

Provide functions for reading and writing CSVW - i.e. CSV tables and JSON metadata. The metadata helps interpret CSV by setting the types and variable names.

In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.

This package implements the CKNNRLD algorithm (Clustering-Based K-Nearest Neighbor Regression for Longitudinal Data) for improving K-Nearest Neighbor ('KNN') regression on longitudinal data through cluster-based partitioning and localized prediction. Offers enhanced computational efficiency and accuracy for high-volume longitudinal datasets. The acronym KNN stands for K-Nearest Neighbor. References: Loeloe MS, Tabatabaei SM, Sefidkar R, Mehrparvar AH, Jambarsang S (2025). "Boosting K-nearest neighbor regression performance for longitudinal data through a novel learning approach." BMC Bioinformatics, 26, 232. <doi:10.1186/s12859-025-06205-1>.

This calculates a variety of different CIs for proportions and difference of proportions that are commonly used in the pharmaceutical industry including Wald, Wilson, Clopper-Pearson, Agresti-Coull and Jeffreys for proportions. And Miettinen-Nurminen (1985) <doi:10.1002/sim.4780040211>, Wald, Haldane, and Mee <https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf> for difference in proportions.