Load configuration from a .env file, that is in the current working directory, into environment variables.

Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches. Changes to data can be made visible by using render_diff(). The V8 package is used to wrap the daff.js JavaScript library which is included in the package.

This package contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

This package provides a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.

This package implements discrete curvature estimation for ordered planar point sequences using circumcenter geometry on consecutive triplets, exposed through compiled C plus plus (C++) code via Rcpp for speed and numerical robustness. The package is useful for objective elbow detection in multivariate workflows, especially principal component analysis (PCA), where selecting the number of retained components can be subjective. It provides a shiny interface that supports upload of raw datasets or explained-variance tables, computes Kaiser-Meyer-Olkin (KMO) sampling-adequacy diagnostics, evaluates individual and cumulative variance curves, and reports curvature- based decision rules (m* and m**) with visual summaries for reproducible component-selection decisions. References: Arney et al. (2001); Axler (2024) <doi:10.1007/978-3-031-41026-0>; Bjorklund (2019) <doi:10.1111/evo.13835>; Burden and Faires (2015); Chang et al. (2023) <https://CRAN.R-project.org/package=shiny>; Christensen (2019); Cui (2020) <doi:10.18637/jss.v040.i08>; Eddelbuettel and Sanderson (2014) <doi:10.1016/j.csda.2013.02.005>; Engelke et al. (2023) <doi:10.1016/j.jseint.2023.04.010>; Gniazdowski (2021) <doi:10.26348/znwwsi.24.35>; Haynes et al. (2017); Jameel and Al-Salami (2023) <doi:10.24086/cuejhss.v7n1y2023.pp121-125>; Jolliffe (2002); Jolliffe and Cadima (2016) <doi:10.1098/rsta.2015.0202>; Kaiser (1974); Lehnert et al. (2019) <doi:10.18637/jss.v089.i12>; Ma and Dai (2011) <doi:10.1093/bib/bbq090>; Milligan (1995); Onumanyi et al. (2022) <doi:10.3390/app12157515>; Park (2010); Revelle (2024) <https://CRAN.R-project.org/package=psych>; Rodionova et al. (2021) <doi:10.1016/j.chemolab.2021.104304>; Sen and Cohen (2025) <doi:10.1177/01466216251344288>; Serneels and Verdonck (2008) <doi:10.1016/j.csda.2007.05.024>; Shi et al. (2021) <doi:10.1186/s13638-021-01910-w>; Shaukat et al. (2016) <doi:10.1515/eko-2016-0014>; Syakur et al. (2018) <doi:10.1088/1757-899X/336/1/012017>; Wickham and Bryan (2023) <https://CRAN.R-project.org/package=readxl>; Wu et al. (2017) <doi:10.1088/1755-1315/61/1/012054>; Youssef et al. (2023) <doi:10.21303/2461-4262.2023.002582>.

Allows to simulate SNP data using genlight objects. For example, it is straight forward to simulate a simple drift scenario with exchange of individuals between two populations or create a new genlight object based on allele frequencies of an existing genlight object.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.

Assesses data quality in Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) databases. Executes data quality checks and provides an R shiny application to view the results.

This package provides methods for working with nominal dates, times, and durations. Base R has sophisticated facilities for handling time, but these can give unexpected results if, for example, timezone is not handled properly. This package provides a more casual approach to support cases which do not require rigorous treatment. It systematically deconstructs the concepts origin and timezone, and de-emphasizes the display of seconds. It also converts among nominal durations such as seconds, hours, days, and weeks. See ?datetime and ?duration for examples. Adapted from metrumrg <http://r-forge.r-project.org/R/?group_id=1215>.

Transform newswire and earnings call transcripts as PDF obtained from Nexis Uni to R data frames. Various newswires and FairDisclosure earnings call formats are supported. Further, users can apply several pre-defined dictionaries on the data based on Graffin et al. (2016)<doi:10.5465/amj.2013.0288> and Gamache et al. (2015)<doi:10.5465/amj.2013.0377>.

Developed to Solve the Multi-Criteria Decision Making Problems with Decision Making Trial and Evaluation Laboratory Technique in R.

Implementation of selected Tidyverse functions within DataSHIELD', an open-source federated analysis solution in R. Currently, DataSHIELD contains very limited tools for data manipulation, so the aim of this package is to improve the researcher experience by implementing essential functions for data manipulation, including subsetting, filtering, grouping, and renaming variables. This is the server-side package which should be installed on the server holding the data, and is used in conjunction with the client-side package dsTidyverseClient which is installed in the local R environment of the analyst. For more information, see <https://tidyverse.org/> and <https://datashield.org/>.

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

Utilities for exporting exercises from R/exams to question pools for the learning management system ILIAS'. The package implements a Question and Test Interoperability 1.2 rendering path tailored to ILIAS and writes the question-pool XML layout validated for imports in ILIAS 9.17. Supported exercise types include single-choice, multiple-choice, numeric, string, and combined gap questions. The underlying R/exams framework is described in Zeileis, Umlauf, and Leisch (2014) <doi:10.18637/jss.v058.i01>.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (fdiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. Also includes two-sample block-wise empirical likelihood (BEL) and a frequency-domain empirical likelihood test for autocorrelation differences (FDEL). Methods for EL, P-P, Q-Q, ROC, qdiff and fdiff are based on Valeinis and Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

The peak fitting of spectral data is performed by using the frame work of EM algorithm. We adapted the EM algorithm for the peak fitting of spectral data set by considering the weight of the intensity corresponding to the measurement energy steps (Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019, 2021 and 2023) <doi:10.1080/14686996.2019.1620123>, <doi:10.1080/27660400.2021.1899449> <doi:10.1080/27660400.2022.2159753>. The package efficiently estimates the parameters of Gaussian mixture model during iterative calculation between E-step and M-step, and the parameters are converged to a local optimal solution. This package can support the investigation of peak shift with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.

This package provides a convenient toolbox to import data exported from Electronic Data Capture (EDC) software TrialMaster'.

This package implements the conditional estimation procedure of Lee, Sun, Sun and Taylor (2016) <doi:10.1214/15-AOS1371>. This procedure allows hypothesis testing on the mean of a normal random vector subject to linear constraints. Also supports computation of the MLE of the mean subject to the same constraints.

This package provides computational tools for working with the Extended Laplace distribution, including the probability density function, cumulative distribution function, quantile function, random variate generation based on convolution with Uniform noise and the quantile-quantile plot. Useful for modeling contaminated Laplace data and other applications in robust statistics. See Saah and Kozubowski (2025) <doi:10.1016/j.cam.2025.116588>.

Analyses EuFMDiS output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt().

This package provides functions for computing critical values and implementing the one-sided/two-sided EL tests.

This new extreme conformal prediction framework provides informative prediction intervals at the high-confidence levels for which classical conformal methods fail. In applications with potentially high-impact events, a very high level of confidence is often required for predictions. If that level is too large relative to the amount of data used for calibration, classical conformal methods provide infinitely wide, thus, uninformative prediction intervals. Our extreme conformal procedure bridges extreme value statistics and conformal prediction to provide reliable and informative prediction intervals with high-confidence coverage, which can be constructed using any black-box extreme quantile regression method. A weighted version of the approach can account for nonstationary data. The methodology was introduced in Pasche, Lam, and Engelke (2026) <doi:10.1007/s10687-026-00536-9>.

This package provides biology-aware lipid set enrichment analysis (LSEA) for lipidomics data using dual engines: the Kolmogorov-Smirnov test and the fast gene set enrichment algorithm from the fgsea package. Annotates lipids into biological groups at three levels (lipid class, LIPID MAPS category, functional category) and tests for coordinated directional shifts between conditions. Includes fatty acid chain analysis with trend plots weighted by lipid abundance (Spearman rank correlation, configurable smoothing), wide-format chain position output (sn-1, sn-2, sn-3, sn-4), annotation confidence filtering, and export utilities for reproducible reporting in CSV, Excel', and PDF formats. Vignettes are available in English and Spanish. Methods are based on Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.

Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, OpenMx'.