Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.

Formats for R Markdown that undo modifications by pandoc and rmarkdown to original latex templates, such as smaller margins, paragraph spacing, and compact titles. In addition, enhancements such as author blocks with affiliations and headers and footers are introduced. All of this functionality is built around plugins that modify the default pandoc template without relying on custom templates.

It provides functions for estimating parameters in linear spatial models with censored or missing responses using the Expectation-Maximization (EM), Stochastic Approximation EM (SAEM), and Monte Carlo EM (MCEM) algorithms. These methods are widely used to obtain maximum likelihood (ML) estimates in the presence of incomplete data. The EM algorithm computes ML estimates when a closed-form expression for the conditional expectation of the complete-data log-likelihood is available. The MCEM algorithm replaces this expectation with a Monte Carlo approximation based on independent simulations of the missing data. In contrast, the SAEM algorithm decomposes the E-step into simulation and stochastic approximation steps, improving computational efficiency in complex settings. In addition, the package provides standard error estimation based on the Louis method. It also includes functionality for spatial prediction at new locations. References used for this package: Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>; Valeriano, K. A., Galarza, C. E., & Matos, L. A. (2023). Moments and random number generation for the truncated elliptical family of distributions. Statistics and Computing, 33(1), 32 <doi:10.1007/s11222-022-10200-4>.

This package implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via rjags (interface to JAGS') and summarized with coda'.

Play the classic game of tic-tac-toe (naughts and crosses).

This package provides tools to download, process, and analyze real-time meteorological radar images from Simepar (Paraná, Brazil) <https://www.simepar.br/simepar/radar_msc>. Designed to support the Rede Agropesquisa hydrological monitoring, it includes functions to detect rainfall intensity based on Red, Green, and Blue (RGB) color values within predefined circular study areas. Features automated integration with the Telegram Bot API <https://core.telegram.org/bots/api> to send spatialized image alerts and an interactive shiny dashboard for easy configuration and continuous weather tracking.

Assess LCâ MS system performance by visualizing instrument log files and monitoring raw quality control samples within a project.

Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. rtmpt is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs).

Enhanced functionality for reactable in shiny applications, offering interactive and dynamic data table capabilities with ease. With reactable.extras', easily integrate a range of functions and components to enrich your shiny apps and facilitate user-friendly data exploration.

The RQuantLib package makes parts of QuantLib accessible from R The QuantLib project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.

Manually bin data using weight of evidence and information value. Includes other binning methods such as equal length, quantile and winsorized. Options for combining levels of categorical data are also available. Dummy variables can be generated based on the bins created using any of the available binning methods. References: Siddiqi, N. (2006) <doi:10.1002/9781119201731.biblio>.

Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.

This package performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes.

Nuclear Decay Data for Dosimetric Calculations from the International Commission on Radiological Protection from ICRP Publication 107. Ann. ICRP 38 (3). Eckerman, Keith and Endo, Akira 2008 <doi:10.1016/j.icrp.2008.10.004> <https://www.icrp.org/publication.asp?id=ICRP%20Publication%20107>. This is a database of the physical data needed in calculations of radionuclide-specific protection and operational quantities. The data is prescribed by the ICRP, the international authority on radiation dose standards, for estimating dose from the intake of or exposure to radionuclides in the workplace and the environment. The database contains information on the half-lives, decay chains, and yields and energies of radiations emitted in nuclear transformations of 1252 radionuclides of 97 elements.

Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. This package prepares these files and also updates the versions according to the branches. It relies heavily on the desc packages.

This package provides a simple WebDAV client that provides functions to fetch and send files or folders to servers using the WebDAV protocol (see RFC 4918 <https://www.rfc-editor.org/rfc/rfc4918>). Only a subset of the protocol is implemented (e.g. file locks are not yet supported).

The receiver operating characteristic (ROC) curve is one of the most widely used tools for evaluating diagnostic and prognostic biomarkers across diverse scientific fields, particularly in medicine. Despite its ubiquity, ROC estimation and testing methods differ substantially in their assumptions and resulting curve properties. This package provides a unified framework for constructing, visualizing, and comparing parametric, nonparametric, semiparametric, and Bayesian ROC curves. ROCModels helps researchers identify and implement ROC inference methods most suitable for their data. See the accompanying vignette ROCModels_Package_Doc for a detailed introduction. Alonzo, T. A., and Pepe, M. S. (2002) <doi: 10.1093/biostatistics/3.3.421>, Andrews, D. F., and Herzberg, A. M. (1985) <doi: 10.1007/978-1-4612-5098-2>, Bamber, D. (1975) <doi: 10.1016/0022-2496(75)90001-2>, Cox, D. R. (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Cox, D. R. (1975) <doi: 10.1093/biomet/62.2.269>, DeLong, E. R., DeLong, D. M., and Clarke-Pearson, D. L. (1988) <doi: 10.2307/2531595>, Dorfman, D. D., and Alf, E. (1969) <doi: 10.1016/0022-2496(69)90019-4>, Dorfman, D. D., Berbaum, K. S., and Metz, C. E. (1997) <doi: 10.1016/s1076-6332(97)80013-x>, Erkanli, A., Sung, L., and Stamey, J. D. (2006) <doi: 10.1002/sim.2496>, Faraggi, D., and Reiser, B. (2002) <doi: 10.1002/sim.1228>, Ghebremichael, M., and Habtemicael, S. (2018) <doi: 10.1080/02664763.2017.1420758>, Ghebremichael, M., and Michael, H. (2024) <doi: 10.1080/03610918.2022.2032159>, Ghebremichael, M., Michael, H., Tubbs, J., and Paintsil, E. (2019) <doi: 10.3844/jmssp.2019.55.64>, Gönen, M., and Heller, G. (2010) <doi: 10.1177/0272989X09360067>, Gopalakrishnan, V., Bose, E., Nair, U., Cheng, Y., and Ghebremichael, M. (2020) <doi: 10.1186/s12879-020-05458-w>, Green, D. M., and Swets, J. A. (1966, ISBN:0471324205), Gu, J., and Ghosal, S. (2009) <doi: 10.1016/j.jspi.2008.09.014>, Gu, Y., Ghosal, S., and Roy, A. (2008) <doi: 10.1002/sim.3366>, Guidoum, A. C. (2020) <doi: 10.32614/CRAN.package.kedd>, <doi: 10.48550/arXiv.2012.06102>, Guo, B. (2015) <https://d-scholarship.pitt.edu/23590/1/Guo_Ben_thesis_12-2014.pdf>, Hanley, J. A., and McNeil, B. J. (1982) <doi: 10.1148/radiology.143.1.7063747>, Hsieh, F., and Turnbull, B. W. (1996) <doi: 10.1214/aos/1033066197>, Hussain, E. (2012) <doi: 10.6000/1927-5129.2012.08.02.09>, Ishwaran, H., and James, L. F. (2002) <doi: 10.1198/106186002411>, Jokiel-Rokita, A., and Topolnicki, R. (2020) <doi: 10.1016/j.csda.2019.106820>, Krzanowski, W. J., and Hand, D. J. (2009) <doi: 10.1201/9781439800225>, Kundu, D., and Gupta, R. D. (2006) <doi: 10.1109/TR.2006.874918>, Lloyd, C. J. (1998) <doi: 10.1080/01621459.1998.10473797>, Lehmann, E. L. (1953) <doi: 10.1214/aoms/1177729080>, Metz, C. E., Herman, B. A., and Shen, J. H. (1998) <doi:10.1002/(SICI)1097-0258(19980515)17:9%3C1033::AID-SIM784%3E3.0.CO;2-Z>, Pepe, M. S. (2003) <doi: 10.1093/oso/9780198509844.001.0001>, Pundir, S., and Amala, R. (2014) <doi: 10.22237/jmasm/1398917940>, Silverman, B. W. (2018) <doi: 10.1201/9781315140919>, Yeo, I. K., and Johnson, R. A. (2000) <doi: 10.1093/biomet/87.4.954>, Zhou, X. H., McClish, D. K., and Obuchowski, N. A. (2009) <doi: 10.1002/9780470906514>, Zou, K. H., Hall, W. J., and Shapiro, D. E. (1997) <doi: 10.1002/(SICI)1097-0258(19971015)16:19%3C2143::AID-SIM655%3E3.0.CO;2-3>.

This package provides data structures and functions for file input/output in the ribios software suite, supporting common bioinformatics and computational biology file formats, designed for fast loading and high performance with minimal dependencies.

This package provides a programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data <http://www.utheria.org/>.

An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available.

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>).

This package provides a robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.

Jalali calendar, or solar Hijri, is calendar of Iran and Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>). This package is designed to working with Jalali date. For this purpose, It defines JalaliDate class that is similar to Date class.