Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

Geostatistical modelling facilities using SpatRaster and SpatVector objects are provided. Non-Gaussian models are fit using INLA', and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>. The RandomFields package is available at <https://www.wim.uni-mannheim.de/schlather/publications/software>.

Preview what a ggplot2 plot would look like if you save it to a file. Attach picture dimensions as a canvas() element and get an instant preview. These dimensions will then be used when you save the plot.

Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.

This package implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Simulation, estimation and testing for geopolitical volatility (GEOVOL) based on the global common volatility model of Engle and Campos-Martins (2023) <doi:10.1016/j.jfineco.2022.09.009>. GEOVOL is modelled as a latent multiplicative volatility factor with heterogeneous factor loadings. Estimation is carried out as a maximization-maximization procedure, where GEOVOL and the GEOVOL loadings are estimated iteratively until convergence.

Designed to simplify geospatial data access from the Statistics Finland Web Feature Service API <https://geo.stat.fi/geoserver/index.html>, the geofi package offers researchers and analysts a set of tools to obtain and harmonize administrative spatial data for a wide range of applications, from urban planning to environmental research. The package contains annually updated time series of municipality key datasets that can be used for data aggregation and language translations.

This package provides functions to explore datasets from the Global Biodiversity Information Facility (GBIF - <https://www.gbif.org/>) using a Shiny interface.

Geographical detectors for measuring spatial stratified heterogeneity, as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the optimal discretization of continuous data, four primary functions of geographical detectors, comparison of size effects of spatial unit and the visualizations of results. To use the package and to refer the descriptions of the package, methods and case datasets, please cite Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has been applied in factor exploration of road performance and multi-scale spatial segmentation for network data, as described in Yongze Song (2018) <doi:10.3390/rs10111696> and Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.

Automatically performs desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and adds the resulting p-values to the plot with an annotation bar. Visualizing group differences are frequently performed by boxplots, bar plots, etc. Statistical test results are often needed to be annotated on these plots. This package provides a convenient function that works on ggplot2 objects, performs the desired statistical test between groups of interest and annotates the test results on the plot.

Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.

This package provides a ggplot2 extension that allows text to follow curved paths. Curved text makes it easier to directly label paths or neatly annotate in polar co-ordinates.

Define, simulate, and validate stock-flow consistent (SFC) macroeconomic models. The godley R package offers tools to dynamically define model structures by adding variables and specifying governing systems of equations. With it, users can analyze how different macroeconomic structures affect key variables, perform parameter sensitivity analyses, introduce policy shocks, and visualize resulting economic scenarios. The accounting structure of SFC models follows the approach outlined in the seminal study by Godley and Lavoie (2007, ISBN:978-1-137-08599-3), ensuring a comprehensive integration of all economic flows and stocks. The algorithms implemented to solve the models are based on methodologies from Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205>, Peressini and Sullivan (1988, ISBN:0-387-96614-5), and contributions by Joao Macalos.

Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.

Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study. Methods included are ANOVA and Average / Range methods. Requires balanced study.

This package provides tools for solving common geocaching puzzle types, and other Geocaching-related tasks.

Perform the Blinder-Oaxaca decomposition for generalized linear model with bootstrapped standard errors. The twofold and threefold decomposition are given, even the generalized linear model output in each group.

Symbolic calculation (addition or multiplication) and evaluation of multivariate polynomials with rational coefficients.

This package provides functions to read in the geometry format under the Neuroimaging Informatics Technology Initiative ('NIfTI'), called GIFTI <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data.

This package provides a template for a geometallurgical database and a fast and easy interface for accessing it.

The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data.

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.

Accurate and computationally efficient p-value calculation methods for a general family of Fisher type statistics (GFisher). The GFisher covers Fisher's combination, Good's statistic, Lancaster's statistic, weighted Z-score combination, etc. It allows a flexible weighting scheme, as well as an omnibus procedure that automatically adapts proper weights and degrees of freedom to a given data. The new p-value calculation methods are based on novel ideas of moment-ratio matching and joint-distribution approximation. The technical details can be found in Hong Zhang and Zheyang Wu (2020) <arXiv:2003.01286>.