Create tibbles and lists of ggplot figures that can be modified as easily as regular ggplot figures. Typical use cases are for creating reports or web pages where many figures are needed with different data and similar formatting.

Train a Gaussian stochastic process model of an unknown function, possibly observed with error, via maximum likelihood or maximum a posteriori (MAP) estimation, run model diagnostics, and make predictions, following Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. (1989) "Design and Analysis of Computer Experiments", Statistical Science, <doi:10.1214/ss/1177012413>. Perform sensitivity analysis and visualize low-order effects, following Schonlau, M. and Welch, W.J. (2006), "Screening the Input Variables to a Computer Model Via Analysis of Variance and Visualization", <doi:10.1007/0-387-28014-6_14>.

Analyze small-sample clustered or longitudinal data using modified generalized estimating equations with bias-adjusted covariance estimator. The package provides any combination of three modified generalized estimating equations and 11 bias-adjusted covariance estimators.

Server implementation of GraphQL <http://spec.graphql.org/>, a query language originally created by Facebook for describing data requirements on complex application data models. Visit <https://graphql.org> to learn more about GraphQL'.

This package provides a collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

This package provides two functions that generate source code implementing the predict function of fitted glm objects. In this version, code can be generated for either C or Java'. The idea is to provide a tool for the easy and fast deployment of glm predictive models into production. The source code generated by this package implements two function/methods. One of such functions implements the equivalent to predict(type="response"), while the second implements predict(type="link"). Source code is written to disk as a .c or .java file in the specified path. In the case of c, an .h file is also generated.

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.

This package provides additional functions for creating beautiful tables with gt'. The functions are generally wrappers around boilerplate or adding opinionated niche capabilities and helpers functions.

This package provides a collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.

Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.

Ranked Set Sampling (RSS) is a stratified sampling method known for its efficiency compared to Simple Random Sampling (SRS). When sample allocation is equal across strata, it is referred to as balanced RSS (BRSS) whereas unequal allocation is called unbalanced RSS (URSS), which is particularly effective for asymmetric or skewed distributions. This package offers practical statistical tools and sampling methods for both BRSS and URSS, emphasizing flexible sampling designs and inference for population means, medians, proportions, and Area Under the Curve (AUC). It incorporates parametric and nonparametric tests, including empirical likelihood ratio (LR) methods. The package provides ranked set sampling methods from a given population, including sampling with imperfect ranking using auxiliary variables. Furthermore, it provides tools for efficient sample allocation in URSS, ensuring greater efficiency than SRS and BRSS. For more details, refer e.g. to Chen et al. (2003) <doi:10.1007/978-0-387-21664-5>, Ahn et al. (2022) <doi:10.1007/978-3-031-14525-4_3>, and Ahn et al. (2024) <doi:10.1111/insr.12589>.

This package provides a framework and functions to create MOODLE quizzes. GIFTr takes dataframe of questions of four types: multiple choices, numerical, true or false and short answer questions, and exports a text file formatted in MOODLE GIFT format. You can prepare a spreadsheet in any software and import it into R to generate any number of questions with HTML', markdown and LaTeX support.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

This package implements statistical methods for group factor analysis, focusing on estimating the number of global and local factors and extracting them. Several algorithms are implemented, including Canonical Correlation-based Estimation by Choi et al. (2021) <doi:10.1016/j.jeconom.2021.09.008>, Generalised Canonical Correlation Estimation by Lin and Shin (2023) <doi:10.2139/ssrn.4295429>, Circularly Projected Estimation by Chen (2022) <doi:10.1080/07350015.2022.2051520>, and the Aggregated Projection Method by Hu et al. (2025) <doi:10.1080/01621459.2025.2491154>.

Supports modeling health outcomes using Bayesian hierarchical spatio-temporal models with complex covariate effects (e.g., linear, non-linear, interactions, distributed lag linear and non-linear models) in the INLA framework. It is designed to help users identify key drivers and predictors of disease risk by enabling streamlined model exploration, comparison, and visualization of complex covariate effects. See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021) <doi:10.1016/S2542-5196(20)30292-8>.

Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The glmnet fitting algorithm is used to fit the continuation ratio model after data restructuring.

Add a scroll back to top Font Awesome icon <https://fontawesome.com/> in rmarkdown documents and shiny apps thanks to jQuery GoTop <https://scottdorman.blog/jquery-gotop/>.

Focuses on data collecting, analyzing and visualization in green finance and environmental risk research and analysis. Main function includes environmental data collecting from official websites such as MEP (Ministry of Environmental Protection of China, <https://www.mee.gov.cn>), water related projects identification and environmental data visualization.

Performing goodness-of-fit tests for stochastic block models used to fit network data. Among the three variants discussed in Karwa et al. (2023) <doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdos-Renyi (ER) and Beta versions.

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package ganGenerativeData <https://cran.r-project.org/package=ganGenerativeData>.

William S. Cleveland's book Visualizing Data is a classic piece of literature on Exploratory Data Analysis. Although it was written several decades ago, its content is still relevant as it proposes several tools which are useful to discover patterns and relationships among the data under study, and also to assess the goodness of fit o a model. This package provides functions to produce the ggplot2 versions of the visualization tools described in this book and is thought to be used in the context of courses on Exploratory Data Analysis.

An interactive git user interface from the R command line. Intuitive tools to make commits, branches, remotes, and diffs an integrated part of R coding. Built on git2r, a system installation of git is not required and has default on-premises remote option.

This package provides functions to estimate model parameters and forecast future volatilities using the Unified GARCH-Ito [Kim and Wang (2016) <doi:10.1016/j.jeconom.2016.05.003>] and Realized GARCH-Ito [Song et. al. (2020) <doi:10.1016/j.jeconom.2020.07.007>] models. Optimization is done using augmented Lagrange multiplier method.

Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.