Extension of ggplot2 providing layers, scales and preprocessing functions useful to represent behavioural variables that are recorded over multiple animals and days. This package is part of the rethomics framework <https://rethomics.github.io/>.

The geom_rain() function adds different geoms together using ggplot2 to create raincloud plots.

This package implements the generalized propensity score cumulative distribution function proposed by Greene (2017) <https://digitalcommons.library.tmc.edu/dissertations/AAI10681743/>. A single scalar balancing score is calculated for any generalized propensity score vector with three or more treatments. This balancing score is used for propensity score matching and stratification in outcome analyses when analyzing either ordinal or multinomial treatments.

Four graph-based tests are provided for testing whether two samples are from the same distribution. It works for both continuous data and discrete data.

Spline regression, generalized additive models and component-wise gradient boosting utilizing geometrically designed (GeD) splines. GeDS regression is a non-parametric method inspired by geometric principles, for fitting spline regression models with variable knots in one or two independent variables. It efficiently estimates the number of knots and their positions, as well as the spline order, assuming the response variable follows a distribution from the exponential family. GeDS models integrate the broader category of generalized (non-)linear models, offering a flexible approach to model complex relationships. A description of the method can be found in Kaishev et al. (2016) <doi:10.1007/s00180-015-0621-7> and Dimitrova et al. (2023) <doi:10.1016/j.amc.2022.127493>. Further extending its capabilities, GeDS's implementation includes generalized additive models (GAM) and functional gradient boosting (FGB), enabling versatile multivariate predictor modeling, as discussed in the forthcoming work of Dimitrova et al. (2025).

The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight.

This package creates tables suitable for regulatory agency submission by leveraging the gtsummary package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from gtsummary as well as custom options designed for regulatory tables.

Fit linear mixed-effects models using restricted (or residual) maximum likelihood (REML) and with generalized inverse matrices to specify covariance structures for random effects. In particular, the package is suited to fit quantitative genetic mixed models, often referred to as animal models'. Implements the average information algorithm as the main tool to maximize the restricted log-likelihood, but with other algorithms available.

This package provides two new layer types for displaying image data as layers within the Grammar of Graphics framework. Displays images using either a rectangle interface, with a fixed bounding box, or a point interface using a central point and general size parameter. Images can be given as local JPEG or PNG files, external resources, or as a list column containing raster image data.

Readable, complete and pretty graphs for correspondence analysis made with FactoMineR'. They can be rendered as interactive HTML plots, showing useful informations at mouse hover. The interest is not mainly visual but statistical: it helps the reader to keep in mind the data contained in the cross-table or Burt table while reading the correspondence analysis, thus preventing over-interpretation. Most graphs are made with ggplot2', which means that you can use the + syntax to manually add as many graphical pieces you want, or change theme elements. 3D graphs are made with plotly'.

This package provides a collection of gold price data in various currencies in the form of USD, EUR, JPY, GBP, CAD, CHF, INR, CNY, TRY, SAR, IDR, AED, THB, VND, EGP, KRW, RUB, ZAR, and AUD. This data comes from the World Gold Council. In addition, the data is in the form of daily, weekly, monthly (average and the end of period), quarterly (average and the end of period), and yearly (average and the end of period).

Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellitesâ positions. x â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk in the equatorial plane in the units of Jupiterâ s equatorial radius; X is positive toward the west, y â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk from the equatorial plane in the units of Jupiterâ s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds.

Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.

The web service at <https://www.geonames.org/> provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist.

This package provides functions for performing polygon geometry with grid grobs. This allows complex shapes to be defined by combining simpler shapes.

It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. It contains functions and methods to import maps and other keywords from geotop.inpts file. Some examples with simulation cases of GEOtop 2.x/3.x are presented in the package. Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation.

Make it easy to create simplified trial summary (TS) domain based on FDA FDA guide <https://github.com/TuCai/phuse/blob/master/inst/examples/07_genTS/www/Simplified_TS_Creation_Guide_v2.pdf>.

An R package for creating panels of diagnostic plots for residuals from a model using ggplot2 and plotly to analyze residuals and model assumptions from a variety of viewpoints. It also allows for the creation of interactive diagnostic plots.

This package provides a quick and easy way of plotting the columns of two matrices or data frames against each other using ggplot2'. Although ggmatplot doesn't provide the same flexibility as ggplot2', it can be used as a workaround for having to wrangle wide format data into long format for plotting with ggplot2'.

This package provides tools for semantic segmentation of geospatial data using convolutional neural network-based deep learning. Utility functions allow for creating masks, image chips, data frames listing image chips in a directory, and DataSets for use within DataLoaders. Additional functions are provided to serve as checks during the data preparation and training process. A UNet architecture can be defined with 4 blocks in the encoder, a bottleneck block, and 4 blocks in the decoder. The UNet can accept a variable number of input channels, and the user can define the number of feature maps produced in each encoder and decoder block and the bottleneck. Users can also choose to (1) replace all rectified linear unit (ReLU) activation functions with leaky ReLU or swish, (2) implement attention gates along the skip connections, (3) implement squeeze and excitation modules within the encoder blocks, (4) add residual connections within all blocks, (5) replace the bottleneck with a modified atrous spatial pyramid pooling (ASPP) module, and/or (6) implement deep supervision using predictions generated at each stage in the decoder. A unified focal loss framework is implemented after Yeung et al. (2022) <doi:10.1016/j.compmedimag.2021.102026>. We have also implemented assessment metrics using the luz package including F1-score, recall, and precision. Trained models can be used to predict to spatial data without the need to generate chips from larger spatial extents. Functions are available for performing accuracy assessment. The package relies on torch for implementing deep learning, which does not require the installation of a Python environment. Raster geospatial data are handled with terra'. Models can be trained using a Compute Unified Device Architecture (CUDA)-enabled graphics processing unit (GPU); however, multi-GPU training is not supported by torch in R'.

Implemented are three Wald-type statistic and respective permuted versions for null hypotheses formulated in terms of cumulative hazard rate functions, medians and the concordance measure, respectively, in the general framework of survival factorial designs with possibly heterogeneous survival and/or censoring distributions, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Ditzhaus, Dobler and Pauly (2020) <doi:10.1177/0962280220980784> Ditzhaus, Janssen, Pauly (2020) <arXiv: 2004.10818v2> Dobler and Pauly (2019) <doi:10.1177/0962280219831316>.

Data from multi environment agronomic trials, which are often carried out by plant breeders, can be analyzed with the tools offered by this package such as the Additive Main effects and Multiplicative Interaction model or AMMI ('Gauch 1992, ISBN:9780444892409) and the Site Regression model or SREG ('Cornelius 1996, <doi:10.1201/9780367802226>). Since these methods present a poor performance under the presence of outliers and missing values, this package includes robust versions of the AMMI model ('Rodrigues 2016, <doi:10.1093/bioinformatics/btv533>), and also imputation techniques specifically developed for this kind of data ('Arciniegas-Alarcón 2014, <doi:10.2478/bile-2014-0006>).

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.

Read examples with interlinear glosses from files or from text and print them in a way compatible with both Latex and HTML outputs.