This package implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.

Anscombe's quartet are a set of four two-variable datasets that have several common summary statistics but which have very different joint distributions. This becomes apparent when the data are plotted, which illustrates the importance of using graphical displays in Statistics. This package enables the creation of datasets that have identical marginal sample means and sample variances, sample correlation, least squares regression coefficients and coefficient of determination. The user supplies an initial dataset, which is shifted, scaled and rotated in order to achieve target summary statistics. The general shape of the initial dataset is retained. The target statistics can be supplied directly or calculated based on a user-supplied dataset. The datasauRus package <https://cran.r-project.org/package=datasauRus> provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics.

To address the violation of the assumption of normally distributed variables, researchers frequently employ bootstrapping. Building upon established packages for R (Sigmann et al. (2024) <doi:10.32614/CRAN.package.afex>, Lenth (2024) <doi:10.32614/CRAN.package.emmeans>), we provide bootstrapping functions to approximate a normal distribution of the parameter estimates for between-subject, within-subject, and mixed one-way and two-way ANOVA.

Visualize results generated by Antares, a powerful open source software developed by RTE to simulate and study electric power systems (more information about Antares here: <https://github.com/AntaresSimulatorTeam/Antares_Simulator>). This package provides functions that create interactive charts to help Antares users visually explore the results of their simulations.

With appRiori <doi:10.1177/25152459241293110>, users upload the research variables and the app guides them to the best set of comparisons fitting the hypotheses, for both main and interaction effects. Through a graphical explanation and empirical examples on reproducible data, it is shown that it is possible to understand both the logic behind the planned comparisons and the way to interpret them when a model is tested.

Set of tools for statistical analysis, visualization, and reporting of agroindustrial and agricultural experiments. The package provides functions to perform one-way and two-way ANOVA with post-hoc tests (Tukey HSD and Duncan MRT), Welch ANOVA for heteroscedastic data, and the Games-Howell post-hoc test as a robust alternative when variance homogeneity fails. Normality of residuals is assessed with the Shapiro-Wilk test and homoscedasticity with the Fligner-Killeen test; the appropriate statistical path is selected automatically based on these diagnostics. Coefficients of variation and statistical power (via one-way ANOVA power analysis) are reported alongside the post-hoc letter display. High-level wrappers allow automated multi-variable analysis with optional clustering by one or two experimental factors, with support for custom level ordering and relabeling. Results are returned as ggplot2 boxplots with mean and letter annotations, wide-format summary tables ready for publication or LaTeX rendering, and structured decision summaries for rapid agronomic interpretation. Direct export to Excel spreadsheets and high-resolution image tables is also supported. Functions follow methods widely used in agronomy, field trials, and plant breeding. Key references: Tukey (1949) <doi:10.2307/3001913>; Duncan (1955) <doi:10.2307/3001478>; Welch (1951) <doi:10.2307/2332579>; Games and Howell (1976) <doi:10.2307/2529858>; Shapiro and Wilk (1965) <doi:10.2307/2333709>; Fligner and Killeen (1976) <doi:10.2307/2529096>; Cohen (1988, ISBN:9781138892899); Wickham (2016, ISBN:9783319242750) for ggplot2'; see also agricolae <https://CRAN.R-project.org/package=agricolae> and rstatix <https://CRAN.R-project.org/package=rstatix>. Version en espanol: Conjunto de herramientas para el analisis estadistico, visualizacion y generacion de reportes en ensayos agroindustriales y agricolas. Incluye ANOVA univariado y bifactorial con pruebas post-hoc (Tukey HSD y Duncan MRT), ANOVA de Welch para datos heterocedasticos y la prueba post-hoc de Games-Howell como alternativa robusta cuando falla la homogeneidad de varianzas. La normalidad de residuos se evalua con la prueba de Shapiro-Wilk y la homogeneidad de varianzas con la prueba de Fligner-Killeen; la ruta estadistica apropiada se selecciona automaticamente segun estos diagnosticos. Se reportan coeficientes de variacion y potencia estadistica junto con las letras de separacion de medias. Los envoltorios de alto nivel permiten analisis multivariable automatizado con agrupamiento opcional por uno o dos factores experimentales, con soporte para orden y etiquetado personalizado de niveles. Los resultados se devuelven como boxplots con anotaciones de medias y letras, tablas resumen en formato ancho listas para publicacion o renderizado en LaTeX, y resumenes de decision para interpretacion agronomica rapida. Tambien se soporta exportacion directa a Excel e imagenes de alta resolucion para informes tecnicos.

This package provides a tool for generating acronyms and initialisms from arbitrary text input.

Functionality to add, delete, read and update table records from your AppSheet apps, using the official API <https://api.appsheet.com/>.

Visualization of Design of Experiments from the agricolae package with ggplot2 framework The user provides an experiment design from the agricolae package, calls the corresponding function and will receive a visualization with ggplot2 based functions that are specific for each design. As there are many different designs, each design is tested on its type. The output can be modified with standard ggplot2 commands or with other packages with ggplot2 function extensions.

This package provides a graphical method for joint visualisation of Variant Set Association Test (VSAT) results and individual variant association statistics. The Archipelago method assigns genomic coordinates to variant set statistics, allowing simultaneous display of variant-level and set-level signals in a unified plot. This supports interpretation of both collective and individual variant contributions in genetic association studies using variant aggregation approaches. For more see Lawless et al. (2026) <doi:10.1002/gepi.70025>.

Download Alphavantage financial data <https://www.alphavantage.co/documentation/> to reduced data.table objects. Includes support functions to extract and simplify complex data returned from API calls.

Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.

Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license.

Collect your data on digital marketing campaigns from Amazon S3 using the Windsor.ai API <https://windsor.ai/api-fields/>.

This toolkit implements a numerical solution algorithm to invert a quality of life measure from observed data. Unlike the traditional Rosen-Roback measure, this measure accounts for mobility frictionsâ generated by idiosyncratic tastes and local ties â and trade frictions â generated by trade costs and non-tradable services, thereby reducing non-classical measurement error. The QoL measure is based on Ahlfeldt, Bald, Roth, Seidel (2024) <https://econpapers.repec.org/RePEc:boc:bocode:s459382> "Measuring Quality of Life under Spatial Frictions". When using this programme or the toolkit in your work, please cite the paper.

This package performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas). Shimizu, G. D., Marubayashi, R. Y. P., Goncalves, L. S. A. (2025) <doi:10.4025/actasciagron.v47i1.73889>.

Data on Asylum and Resettlement for the UK, provided by the Home Office <https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables>.

Amiga Disk Files (ADF) are virtual representations of 3.5 inch floppy disks for the Commodore Amiga. Most disk drives from other systems (including modern drives) are not able to read these disks. The adfExplorer package enables you to establish R connections to files on such virtual DOS-formatted disks, which can be use to read from and write to those files.

Display air quality model output and monitoring data using scatterplots, grids, and legends.

Edit an Antares simulation before running it : create new areas, links, thermal clusters or binding constraints or edit existing ones. Update Antares general & optimization settings. Antares is an open source power system generator, more information available here : <https://antares-simulator.org/>.

This package provides a toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package focusing on the metabolism therapeutic area.

This package provides a modeling package compiling applicability domain methods in R. It combines different methods to measure the amount of extrapolation new samples can have from the training set. See <doi:10.4018/IJQSPR.2016010102> for an overview of applicability domains.

This package provides tools for assessing and selecting auxiliary variables using LASSO. The package includes functions for variable selection and diagnostics, facilitating survey calibration analysis with emphasis on robust auxiliary vector selection. For more details see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and Caughrey and Hartman (2017) <doi:10.2139/ssrn.3494436>.

Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.