This package provides a ggplot2 extension providing an integrative framework for composable visualization, enabling the creation of complex multi-plot layouts such as insets, circular arrangements, and multi-panel compositions. Built on the grammar of graphics, it offers tools to align, stack, and nest plots, simplifying the construction of richly annotated figures for high-dimensional data contextsâ such as genomics, transcriptomics, and microbiome studiesâ by making it easy to link related plots, overlay clustering results, or highlight shared patterns.

Fits a geographically weighted regression model using zero inflated probability distributions. Has the zero inflated negative binomial distribution (zinb) as default, but also accepts the zero inflated Poisson (zip), negative binomial (negbin) and Poisson distributions. Can also fit the global versions of each regression model. Da Silva, A. R. & De Sousa, M. D. R. (2023). "Geographically weighted zero-inflated negative binomial regression: A general case for count data", Spatial Statistics <doi:10.1016/j.spasta.2023.100790>. Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). "Geographically weighted regression: a method for exploring spatial nonstationarity", Geographical Analysis, <doi:10.1111/j.1538-4632.1996.tb00936.x>. Yau, K. K. W., Wang, K., & Lee, A. H. (2003). "Zero-inflated negative binomial mixed regression modeling of over-dispersed count data with extra zeros", Biometrical Journal, <doi:10.1002/bimj.200390024>.

Implementing generalized structured component analysis (GSCA) and its basic extensions, including constrained single and multiple group analysis, and second order latent variable modeling. For a comprehensive overview of GSCA, see Hwang & Takane (2014, ISBN: 9780367738754).

Utilizes methods of the PyMongo Python library to initialize, insert and query GeoJson data (see <https://github.com/mongodb/mongo-python-driver> for more information on PyMongo'). Furthermore, it allows the user to validate GeoJson objects and to use the console for MongoDB (bulk) commands. The reticulate package provides the R interface to Python modules, classes and functions.

Scrapes Google Citation pages and creates data frames of citations over time.

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

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.

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'.

Command-line and shiny GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

This package contains functions to create life history parameter plots from raw data. The plots are created using ggplot2', and calculations done using the tidyverse collection of packages. The package contains references to FishBase (Froese R., Pauly D., 2023) <https://www.fishbase.se/>.

Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package bamlss via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) <doi:10.1080/10618600.2017.1407325>).

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.

In computationally demanding data analysis pipelines, the targets R package (2021, <doi:10.21105/joss.02959>) maintains an up-to-date set of results while skipping tasks that do not need to rerun. This process increases speed and increases trust in the final end product. However, it also overwrites old output with new output, and past results disappear by default. To preserve historical output, the gittargets package captures version-controlled snapshots of the data store, and each snapshot links to the underlying commit of the source code. That way, when the user rolls back the code to a previous branch or commit, gittargets can recover the data contemporaneous with that commit so that all targets remain up to date.

Simplifies the process of creating essential visualizations in R, offering a range of plotting functions for common chart types like violin plots, pie charts, and histograms. With an intuitive interface, users can effortlessly customize colors, labels, and styles, making it an ideal tool for both beginners and experienced data analysts. Whether exploring datasets or producing quick visual summaries, this package provides a streamlined solution for fundamental graphics in R.

Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.

Automates the process of adding, committing, and pushing changes to a git repository using commit messages generated by passing the git diff output to the OpenAI GPT-3.5 Turbo model (<https://platform.openai.com/docs/models/gpt-3>).

Provide specialized ggplot2 layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

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

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

The gasanalyzer R package offers methods for importing, preprocessing, and analyzing data related to photosynthetic characteristics (gas exchange, chlorophyll fluorescence and isotope ratios). It translates variable names into a standard format, and can recalculate derived, physiological quantities using imported or predefined equations. The package also allows users to assess the sensitivity of their results to different assumptions used in the calculations. See also Tholen (2024) <doi:10.1093/aobpla/plae035>.

This package provides a plain Rcpp wrapper for MeCab that can segment Chinese, Japanese, and Korean text into tokens. The main goal of this package is to provide an alternative to tidytext using morphological analysis.

It can be necessary to limit the rate of execution of a loop or repeated function call e.g. to show or gather data only at particular intervals. This package includes two methods for limiting this execution rate; speed governors and timers. A speed governor will insert pauses during execution to meet a user-specified loop time. Timers are alarm clocks which will indicate whether a certain time has passed. These mechanisms are implemented in C to minimize processing overhead.

Computes the probability density, survival function, the hazard rate functions and generates random samples from the GTDL distribution given by Mackenzie, G. (1996) <doi:10.2307/2348408>. The likelihood estimates, the randomized quantile (Louzada, F., et al. (2020) <doi:10.1109/ACCESS.2020.3040525>) residuals and the normally transformed randomized survival probability (Li,L., et al. (2021) <doi:10.1002/sim.8852>) residuals are obtained for the GTDL model.

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.