Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

This package provides a novel visualization technique for plotting timestamped events on a 24-hour circular clock face. This is particularly useful for analyzing daily patterns, event clustering, and gaps in temporal data. The package also generalizes this approach to create cyclic charts for other periods, including weekly and monthly cycles, enabling effective event planning and pattern analysis across multiple time frames.

This package provides an R interface to the CVD Prevent application programming interface (API), allowing users to retrieve and analyse cardiovascular disease prevention data from primary care records across England. The Cardiovascular Disease Prevention Audit (CVDPREVENT) automatically extracts routinely held GP health data to support national reporting and improvement initiatives. See the API documentation for details: <https://bmchealthdocs.atlassian.net/wiki/spaces/CP/pages/317882369/CVDPREVENT+API+Documentation>.

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.

Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

Method for fitting a cellwise robust linear M-regression model (CRM, Filzmoser et al. (2020) <DOI:10.1016/j.csda.2020.106944>) that yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields an imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The package also provides diagnostic tools for analyzing casewise and cellwise outliers using sparse directions of maximal outlyingness (SPADIMO, Debruyne et al. (2019) <DOI:10.1007/s11222-018-9831-5>).

This package provides constructions of series of partially balanced incomplete block designs (PBIB) based on the combinatory method S, introduced by Rezgui et al. (2014) <doi:10.3844/jmssp.2014.45.48>. This package also offers the associated U-type designs. Version 1.1-1 generalizes the approach to designs with v = wnl treatments. It includes various rectangular and generalized rectangular right angular association schemes with 4, 5, and 7 associated classes.

Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.

This package provides a standard test is observed on all specimens. We treat the second test (or sampled test) as being conducted on only a stratified sample of specimens. Verification Bias is this situation when the specimens for doing the second (sampled) test is not under investigator control. We treat the total sample as stratified two-phase sampling and use inverse probability weighting. We estimate diagnostic accuracy (category-specific classification probabilities; for binary tests reduces to specificity and sensitivity, and also predictive values) and agreement statistics (percent agreement, percent agreement by category, Kappa (unweighted), Kappa (quadratic weighted) and symmetry tests (reduces to McNemar's test for binary tests)). See: Katki HA, Li Y, Edelstein DW, Castle PE. Estimating the agreement and diagnostic accuracy of two diagnostic tests when one test is conducted on only a subsample of specimens. Stat Med. 2012 Feb 28; 31(5) <doi:10.1002/sim.4422>.

This package provides a set of functions to fit a boosting conditional logit model.

The Genetic Algorithm (GA) is used to perform changepoint analysis in time series data. The package also includes an extended island version of GA, as described in Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). By mimicking the principles of natural selection and evolution, GA provides a powerful stochastic search technique for solving combinatorial optimization problems. In changepointGA', each chromosome represents a changepoint configuration, including the number and locations of changepoints, hyperparameters, and model parameters. The package employs genetic operatorsâ selection, crossover, and mutationâ to iteratively improve solutions based on the given fitness (objective) function. Key features of changepointGA include encoding changepoint configurations in an integer format, enabling dynamic and simultaneous estimation of model hyperparameters, changepoint configurations, and associated parameters. The detailed algorithmic implementation can be found in the package vignettes and in the paper of Li (2024, <doi:10.48550/arXiv.2410.15571>).

Simple, fast, and automatic encodings for category data using a data.table backend. Most of the methods are an implementation of "Sufficient Representation for Categorical Variables" by Johannemann, Hadad, Athey, Wager (2019) <arXiv:1908.09874>, particularly their mean, sparse principal component analysis, low rank representation, and multinomial logit encodings.

Clusters longitudinal trajectories over time (can be unequally spaced, unequal length time series and/or partially overlapping series) on a common time axis. Performs k-means clustering on a single continuous variable measured over time, where each mean is defined by a thin plate spline fit to all points in a cluster. Distance is MSE across trajectory points to cluster spline. Provides graphs of derived cluster splines, silhouette plots, and Adjusted Rand Index evaluations of the number of clusters. Scales well to large data with multicore parallelism available to speed computation.

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.

Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.

Measuring cellular energetics is essential to understanding a matrixâ s (e.g. cell, tissue or biofluid) metabolic state. The Agilent Seahorse machine is a common method to measure real-time cellular energetics, but existing analysis tools are highly manual or lack functionality. The Cellular Energetics Analysis Software (ceas) R package fills this analytical gap by providing modular and automated Seahorse data analysis and visualization using the methods described by Mookerjee et al. (2017) <doi:10.1074/jbc.m116.774471>.

Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks.

Prints code that can be used to recreate R objects. In a sense it is similar to base::dput() or base::deparse() but constructive strives to use idiomatic constructors.

Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) <doi:10.21105/joss.00087>.

Maps one of the viridis colour palettes, or a user-specified palette to values. Viridis colour maps are created by Stéfan van der Walt and Nathaniel Smith, and were set as the default palette for the Python Matplotlib library <https://matplotlib.org/>. Other palettes available in this library have been derived from RColorBrewer <https://CRAN.R-project.org/package=RColorBrewer> and colorspace <https://CRAN.R-project.org/package=colorspace> packages.

This package provides a fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the a la carte embeddings approach developed by Khodak et al. (2018) <doi:10.48550/arXiv.1805.05388> and evaluate hypotheses about covariate effects on embeddings using the regression framework developed by Rodriguez et al. (2021)<doi:10.1017/S0003055422001228>. New version of the package applies a new estimator to measure the distance between word embeddings as described in Green et al. (2025) <doi:10.1017/pan.2024.22>.

Access chemical, hazard, bioactivity, and exposure data from the Computational Toxicology and Exposure ('CTX') APIs <https://api-ccte.epa.gov/docs/>. ccdR was developed to streamline the process of accessing the information available through the CTX APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the EPA Computational Toxicology and Exposure Online Resources <https://www.epa.gov/comptox-tools>.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.