Processing collections of Earth observation images as on-demand multispectral, multitemporal raster data cubes. Users define cubes by spatiotemporal extent, resolution, and spatial reference system and let gdalcubes automatically apply cropping, reprojection, and resampling using the Geospatial Data Abstraction Library ('GDAL'). Implemented functions on data cubes include reduction over space and time, applying arithmetic expressions on pixel band values, moving window aggregates over time, filtering by space, time, bands, and predicates on pixel values, exporting data cubes as netCDF or GeoTIFF files, plotting, and extraction from spatial and or spatiotemporal features. All computational parts are implemented in C++, linking to the GDAL', netCDF', CURL', and SQLite libraries. See Appel and Pebesma (2019) <doi:10.3390/data4030092> for further details.

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients as presented by Sarrias and Daziano (2017) <doi:10.18637/jss.v079.i02>. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.

This package provides functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.

This package provides functions and a graphical user interface for graphical described multiple test procedures.

Estimate Gaussian graphical models with nonconvex penalties, including methods described by Williams (2020) <doi:10.31234/osf.io/ad57p>. Penalties include atan (Wang and Zhu, 2016) <doi:10.1155/2016/6495417>, seamless L0 (Dicker, Huang and Lin, 2013) <doi:10.5705/ss.2011.074>, exponential (Wang, Fan and Zhu, 2018) <doi:10.1007/s10463-016-0588-3>, smooth integration of counting and absolute deviation (Lv and Fan, 2009) <doi:10.1214/09-AOS683>, logarithm (Mazumder, Friedman and Hastie, 2011) <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation (Fan and Li, 2001) <doi:10.1198/016214501753382273>, and minimax concave penalty (Zhang, 2010) <doi:10.1214/09-AOS729>. The package also provides extensions for variable inclusion probabilities, multiple regression coefficients, and statistical inference (Janková and van de Geer, 2015) <doi:10.1214/15-EJS1031>.

Create interactive visualization charts to draw data in three dimensional graphs. The graphs can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio Viewer. Based on the vis.js Graph3d module and the htmlwidgets R package.

The aim of this package is to offer more variability of graphics based on the self-organizing maps.

Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.

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>.

Applies sequential clustering algorithm to animal location data based on user-defined parameters. Plots interactive cluster maps and provides a summary dataframe with attributes for each cluster commonly used as covariates in subsequent modeling efforts. Additional functions provide individual keyhole markup language plots for quick assessment, and export of global positioning system exchange format files for navigation purposes. Methods can be found at <doi:10.1111/2041-210X.13572>.

This package provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in RStudio', R Markdown documents, and R Jupyter Notebooks'. Internally, uses MusicXML <https://github.com/w3c/musicxml> to represent music, and MuseScore <https://musescore.org/> to convert MusicXML'.

Estimation of the effect of each income source on income inequalities based on the decomposition of Lerman and Yitzhaki (1985) <doi:10.2307/1928447>.

This package provides a set of high efficient functions to decode identifiers of National Football League players.

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Una coleccion de conjuntos de datos para el libro "Graficas versatiles con ggplot: Analisis visuales de datos", por Raymond L. Tremblay y Julian Hernandez-Serrano. Incluye datos de ecologia, salud publica, educacion, economia y biodiversidad para la ensenanza de visualizacion de datos con ggplot2'.

This package provides functions to produce ggplot2'-based plots of objects produced by functions in the vegan package. Provides fortify()', autoplot()', and tidy() methods for many of vegan''s functions. The aim of ggvegan is to make it easier to work within the tidyverse with vegan'.

Extensions to ggplot2 providing low-level debug tools: statistics and geometries echoing their data argument. Layer manipulation: deletion, insertion, extraction and reordering of layers. Deletion of unused variables from the data object embedded in "ggplot" objects.

This package provides tools.

An interface to the Gmail RESTful API. Allows access to your Gmail messages, threads, drafts and labels.

Multiple comparison procedures (MCPs) control the familywise error rate in clinical trials. Graphical MCPs include many commonly used procedures as special cases; see Bretz et al. (2011) <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency implementation of graphical MCPs which allow mixed types of tests. It also includes power simulations and visualization of graphical MCPs.

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

This package provides statistical methods to check if a parametric family of conditional density functions fits to some given dataset of covariates and response variables. Different test statistics can be used to determine the goodness-of-fit of the assumed model, see Andrews (1997) <doi:10.2307/2171880>, Bierens & Wang (2012) <doi:10.1017/S0266466611000168>, Dikta & Scheer (2021) <doi:10.1007/978-3-030-73480-0> and Kremling & Dikta (2024) <doi:10.48550/arXiv.2409.20262>. As proposed in these papers, the corresponding p-values are approximated using a parametric bootstrap method.

This package provides tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.

An R package that allows for combining tree-boosting with Gaussian process and mixed effects models. It also allows for independently doing tree-boosting as well as inference and prediction for Gaussian process and mixed effects models. See <https://github.com/fabsig/GPBoost> for more information on the software and Sigrist (2022, JMLR) <https://www.jmlr.org/papers/v23/20-322.html> and Sigrist (2023, TPAMI) <doi:10.1109/TPAMI.2022.3168152> for more information on the methodology.