This package provides a port of the C++ routine for applying the marching cubes algorithm written by Thomas Lewiner et al. (2012) <doi:10.1080/10867651.2003.10487582> into an R package. The package supplies the contour3d() function, which takes a 3-dimensional array of voxel data and calculates the vertices, vertex normals, and faces for a 3d mesh representing the contour(s) at a given level.

Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services (<https://ec.europa.eu/eurostat/web/user-guides/data-browser/api-data-access/api-detailed-guidelines/sdmx2-1>) to search and download data from the Eurostat database using the SDMX standard.

This package implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported.

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

This package provides a set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data.

Real-time quantitative polymerase chain reaction (qPCR) data by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The data comprises a six-point, ten-fold dilution series, repeated in five independent runs, for two different amplicons. In each run, each standard concentration is replicated four times. For the original raw data file see the Supplementary Data section: <https://academic.oup.com/nar/article/32/22/e178/2375678#supplementary-data>.

Response surface designs with neighbour effects are suitable for experimental situations where it is expected that the treatment combination administered to one experimental unit may affect the response on neighboring units as well as the response on the unit to which it is applied (Dalal et al.,2025 <doi: 10.57805/revstat.v23i2.513>). Integrating these effects in the response surface model improves the experiment's precision Verma A., Jaggi S., Varghese, E.,Varghese, C.,Bhowmik, A., Datta, A. and Hemavathi M. (2021)<doi: 10.1080/03610918.2021.1890123>). This package includes sym(), asym1(), asym2(), asym3() and asym4() functions that generates response surface designs which are rotatable under a polynomial model of a given order without interaction term incorporating neighbour effects.

Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.

This package provides functions to construct efficient row-column designs for 3-level factorial experiments in 3 rows. The designs ensure the estimation of all main effects (full efficiency) and two factor interactions in minimum replications. For more details, see Dey, A. and Mukerjee, R. (2012) <doi:10.1016/j.spl.2012.06.014> and Dash, S., Parsad, R., and Gupta, V. K. (2013) <doi:10.1007/s40003-013-0059-5>.

This package provides a comprehensive set of regular expression functions based on those found in Python without relying on reticulate'. It provides functions that intend to (1) make it easier for users familiar with Python to work with regular expressions, (2) reduce the complexity often associated with regular expressions code, (3) and enable users to write more readable and maintainable code that relies on regular expression-based pattern matching.

Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.

Reads in text from unstructured modern Microsoft Office files (XML based files) such as Word and PowerPoint. This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.

This package provides a comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

It provides a function for random number generation from members of the truncated multivariate elliptical family of distributions, including truncated versions of the Normal, Student-t, Pearson type VII, Slash, Logistic, and related distributions. Additional distributions can be specified by supplying the density generating function. The package also computes first- and second-order moments, including the covariance matrix, for selected distributions. 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>; Ho, H. J., Lin, T. I., Chen, H. Y., & Wang, W. L. (2012). Some results on the truncated multivariate t distribution. Journal of Statistical Planning and Inference, 142(1), 25-40 <doi:10.1016/j.jspi.2011.06.006>; 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>.

Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. rdss includes datasets, helper functions, and plotting components to enable use and replication of the book.

Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps. Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.

Create tests and tasks compliant with the Question & Test Interoperability (QTI) information model version 2.1. Input sources are Rmd/md description files or S4-class objects. Output formats include standalone zip or xml files. Supports the generation of basic task types (single and multiple choice, order, pair association, matching tables, filling gaps and essay) and provides a comprehensive set of attributes for customizing tests.

The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. <DOI: 10.1080/00273170802285693>. <DOI: 10.1037/a0025697>.

Additional matrix functionality for R including: (1) wrappers for the base matrix function that allow matrices to be created from character strings and lists (the former is especially useful for creating block matrices), (2) better printing of large matrices via the generic "pretty" print function, and (3) a number of convenience functions for users more familiar with other scientific languages like Julia', Matlab'/'Octave', or Python'+'NumPy'.

This package provides methods to calculate approximate regional consistency probabilities using Method 1 and Method 2 proposed by the Japanese Ministry of Health, Labor and Welfare (2007) <https://www.pmda.go.jp/files/000153265.pdf>. These methods are useful for assessing regional consistency in multi-regional clinical trials. The package can calculate unconditional, joint, and conditional regional consistency probabilities. For technical details, please see Homma (2024) <doi:10.1002/pst.2358>.

This package provides functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.

Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.

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.

This package implements the hierarchical Bayesian analysis of populations structure (hierBAPS) algorithm of Cheng et al. (2013) <doi:10.1093/molbev/mst028> for clustering DNA sequences from multiple sequence alignments in FASTA format. The implementation includes improved defaults and plotting capabilities and unlike the original MATLAB version removes singleton SNPs by default.