The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Git hook scripts are useful for identifying simple issues before submission to code review. captain (hook) is an R package to manage and run git pre-commit hooks.

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.

This package provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.

For multiple testing. Computes estimates and confidence bounds for the False Discovery Proportion (FDP), the fraction of false positives among all rejected hypotheses. The methods in the package use permutations of the data. Doing so, they take into account the dependence structure in the data.

This package provides a generic sleepâ wake cycle detection algorithm for analyzing unlabeled actigraphy data. The algorithm has been validated against event markers using data from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study, and its methodological details are described in Chen and Sun (2024) <doi:10.1098/rsos.231468>. The package provides functions to estimate sleep metrics (e.g., sleep and wake onset times) and circadian rhythm metrics (e.g., mesor, phasor, interdaily stability, intradaily variability), as well as tools for screening actigraphy quality, fitting cosinor models, and performing parametric change point detection. The workflow can also be used to segment long actigraphy sequences into regularized structures for physical activity research.

Sample size estimation in cluster (group) randomized trials. Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).

Constructs co-occurrence networks from several types of input data, such as delimited fields, long/bipartite tables, binary matrices, or wide sequences. Returns tidy edge data frames and supports optional scaling, splitting into several networks, thresholding, and subsetting. Provides eight similarity measures, including Jaccard, cosine, and association strength. Supports export to several network and file formats. Network construction and analysis methods follow Saqr, Lopez-Pernas, Conde, and Hernandez-Garcia (2024, <doi:10.1007/978-3-031-54464-4_15>).

Extends ACER ConQuest through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call ACER ConQuest from within R and read ACER ConQuest System Files (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>). Requires ACER ConQuest version 5.40 or later. A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.

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.

Covariate Assisted Principal Regression (CAPR) for multiple covariance-matrix outcomes. The method identifies (principal) projection directions that maximize the log-likelihood of a log-linear regression model of the covariates. See Zhao et al. (2021), "Covariate Assisted Principal Regression for Covariance Matrix Outcomes" <doi:10.1093/biostatistics/kxz057>.

The Concordance Test is a non-parametric method for testing whether two o more samples originate from the same distribution. It extends the Kendall Tau correlation coefficient when there are only two groups. For details, see Alcaraz J., Anton-Sanchez L., Monge J.F. (2022) The Concordance Test, an Alternative to Kruskal-Wallis Based on the Kendall-tau Distance: An R Package. The R Journal 14, 26â 53 <doi:10.32614/RJ-2022-039>.

Fast C++'-backed tools for computing conspecific and total neighborhood basal area in mapped forest plots. Includes unweighted and distance-weighted neighborhoods, multiple radii, decay kernels, and basic edge correction. Outputs are model-ready covariates for forest competition, growth, and survival models, following neighborhood modeling workflows commonly used in spatial ecology (e.g., Hülsmann et al. 2024 <doi:10.1038/s41586-024-07118-4>).

Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) <doi:10.1007/s11634-018-0324-3>.

This package provides functions to perform missing value imputation with compositional data using the Jensen-Shannon divergence based k--NN and a--k--NN algorithms. The functions are based on the following paper: Tsagris M., Alenazi A. and Stewart C. (2026). "A Jensen--Shannon divergence based k--NN algorithm for missing value imputation in compositional data". Journal of Applied Statistics (Accepted for publication).

Cluster analysis with compositional data using the alpha--transformation. Relevant papers include: Tsagris M. and Kontemeniotis N. (2025), <doi:10.48550/arXiv.2509.05945>. Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>. Garcia-Escudero Luis A., Gordaliza Alfonso, Matran Carlos, Mayo-Iscar Agustin. (2008), <doi:10.1214/07-AOS515>.

This package provides a tool for transforming coordinates in a color space to common color names using data from the Royal Horticultural Society and the International Union for the Protection of New Varieties of Plants.

Manages comparison of MCMC performance metrics from multiple MCMC algorithms. These may come from different MCMC configurations using the nimble package or from other packages. Plug-ins for JAGS via rjags and Stan via rstan are provided. It is possible to write plug-ins for other packages. Performance metrics are held in an MCMCresult class along with samples and timing data. It is easy to apply new performance metrics. Reports are generated as html pages with figures comparing sets of runs. It is possible to configure the html pages, including providing new figure components.

This package provides a graphical user interface for simulating the effects of mergers, tariffs, and quotas under an assortment of different economic models. The interface is powered by the Shiny web application framework from RStudio'.

Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

This package provides tools for storing and managing competition results. Competition is understood as a set of games in which players gain some abstract scores. There are two ways for storing results: in long (one row per game-player) and wide (one row per game with fixed amount of players) formats. This package provides functions for creation and conversion between them. Also there are functions for computing their summary and Head-to-Head values for players. They leverage grammar of data manipulation from dplyr'.

There are 6 novel robust tests for equal correlation. They are all based on logistic regressions. The score statistic U is proportion to difference of two correlations based on different types of correlation in 6 methods. The ST1() is based on Pearson correlation. ST2() improved ST1() by using median absolute deviation. ST3() utilized type M correlation and ST4() used Spearman correlation. ST5() and ST6() used two different ways to combine ST3() and ST4(). We highly recommend ST5() according to the article titled New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs published in Scientific Reports. Please see the reference: Yu et al. (2019) <doi:10.1038/s41598-019-40167-8>.

This package provides functions for classical test theory analysis, following methods presented by Wu et al. (2006) <doi:10.1007/978-981-10-3302-5>.