In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

This package provides a port of Ruby Warrior. Teaches R programming in a fun and interactive way.

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

Minirhizotrons are widely used to observe and explore roots and their growth. This package provides the means to stitch images and divide them into depth layers. Please note that this R package was developed alongside the following manuscript: Stitching root scans and extracting depth layer information -- a workflow and practical examples, S. Kersting, L. Knüver, and M. Fischer. The manuscript is currently in preparation and should be citet as soon as it is available. This project was supported by the project ArtIGROW, which is a part of the WIR!-Alliance ArtIFARM â Artificial Intelligence in Farming funded by the German Federal Ministry of Research, Technology and Space (No. 03WIR4805).

We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

The goal of the readelan is to provide a simple way to read data and metadata in files created with the annotation software ELAN <https://archive.mpi.nl/tla/elan> into R as data frames.

Allows for production of Czekanowski's Diagrams with clusters. See K. Bartoszek, A. Vasterlund (2020) <doi:10.2478/bile-2020-0008> and K. Bartoszek, Y. Luo (2023) <doi:10.14708/ma.v51i2.7259>. The suggested FuzzyDBScan package (which allows for fuzzy clustering) can be obtained from <https://github.com/henrifnk/FuzzyDBScan/> (or from CRAN's Archive <https://cran.r-project.org/src/contrib/Archive/FuzzyDBScan/>).

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

According to the order of the loci on the chromosome, the loci can be connected according to the interrelationship between them and classified according to different locus types.

Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1) "Mathematical Statistics with Resampling in R" (3rd Ed).

This package performs wood cell anatomical data analyses on spatially explicit xylem (tracheids) datasets derived from thin sections of woody tissue. The package includes functions for visualisation, detection and alignment of continuous tracheid radial file (defined as rows) and individual tracheid position within an annual ring of coniferous species. This package is designed to be used with elaborate cell output, e.g. as provided with ROXAS (von Arx & Carrer, 2014 <doi:10.1016/j.dendro.2013.12.001>). The package has been validated for Picea abies, Larix Siberica, Pinus cembra and Pinus sylvestris.

Access to the C-level R date and datetime code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header RApiDatetime.h provided by this package, and also import it. The R Core group is the original author of the code made available with slight modifications by this package.

Summarise results from simulation studies and compute Monte Carlo standard errors of commonly used summary statistics. This package is modelled on the simsum user-written command in Stata (White I.R., 2010 <https://www.stata-journal.com/article.html?article=st0200>), further extending it with additional performance measures and functionality.

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.

This package provides estimation and data generation tools for several new regression models, including the gamma, beta, inverse gamma, beta prime, log-normal and log-logistic distributions. These models can be parameterized based on the mean, median, mode, geometric mean and harmonic mean, except for the log-logistic model which is based on alternative parametrizations. For details, see Bourguignon and Gallardo (2025a) <doi:10.1016/j.chemolab.2025.105382> and Bourguignon and Gallardo (2025b) <doi:10.1111/stan.70007>. The package also implements higher-order likelihood inference through Skovgaard-adjusted likelihood ratio statistics and predictive shrinkage estimators reparameterized beta regression models.

Loading data from tiktok Marketing API <https://business-api.tiktok.com/portal> by business centers, advertisers, budgets and reports.

Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities.

Perform optimal transport on somatic point mutations and kernel regression hypothesis testing by integrating pathway level similarities at the gene level (Little et al. (2023) <doi:10.1111/biom.13769>). The software implements balanced and unbalanced optimal transport and omnibus tests with C++ across a set of tumor samples and allows for multi-threading to decrease computational runtime.

This package performs species distribution modeling for rare species with unprecedented accuracy (Mondanaro et al., 2023 <doi:10.1111/2041-210X.14066>) and finds the area of origin of species and past contact between them taking climatic variability in full consideration (Mondanaro et al., 2025 <doi:10.1111/2041-210X.14478>).

Plot rpart models. Extends plot.rpart() and text.rpart() in the rpart package.

Supports modelling case data to facilitate. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Monte Carlo maximum likelihood is the main fitting method with a low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and a stochastic partial differential equation approximation. Bayesian methods are also provided for some methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441>.

Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It offers full access to txt, csv, xml and spreadsheets files which are meant to be read by JDemetra+ Graphical User Interface.

The function runMCMC_btadjust() returns a mcmc.list object which is the output of a Markov Chain Monte Carlo obtained - from either JAGS', nimble or greta - after adjusting burn-in and thinning parameters to meet pre-specified criteria in terms of convergence & effective sample size. Used with nimble', runMCMC_btadjust() allows extra calculations (e.g. information criteria for model comparison and goodness-of-fit p-values for model diagnosis).