Interface for the GitHub API that enables efficient management of courses on GitHub. It has a functionality for managing organizations, teams, repositories, and users on GitHub and helps automate most of the tedious and repetitive tasks around creating and distributing assignments.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

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.

Discretize multivariate continuous data using a grid to capture the joint distribution that preserves clusters in original data. It can handle both labeled or unlabeled data. Both published methods (Wang et al 2020) <doi:10.1145/3388440.3412415> and new methods are included. Joint grid discretization can prepare data for model-free inference of association, function, or causality.

Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.

This package provides geographical faceting functionality for ggplot2'. Geographical faceting arranges a sequence of plots of data for different geographical entities into a grid that preserves some of the geographical orientation.

Wrapper ggplot2 functions for publication-quality visualisation. Aligned with ggplot2 and tidyverse'. Covers much of what ggplot2 does.

This package provides tools to download datasets of German elections covering local, state, federal, mayoral, European Parliament, and county (Kreistag) elections, with federal county-level coverage from 1953 and other families extending through 2025. The package supplies turnout, vote shares, and derived indicators at the municipal and county level, including geographically harmonized datasets that account for changes in municipal boundaries over time and incorporate mail-in voting districts. Bundled data includes county-level INKAR covariates (1995-2022) and municipality-level Zensus 2022 indicators. Data is sourced from <https://github.com/awiedem/german_election_data>.

Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). GeneScape incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.

Turn irregular polygons (such as geographical regions) into regular or hexagonal grids. This package enables the generation of regular (square) and hexagonal grids through the package sp and then assigns the content of the existing polygons to the new grid using the Hungarian algorithm, Kuhn (1955) (<doi:10.1007/978-3-540-68279-0_2>). This prevents the need for manual generation of hexagonal grids or regular grids that are supposed to reflect existing geography.

Mapping tools that convert place names to coordinates on the fly. These ggplot2 extensions make maps from a data frame where one of the columns contains place names, without having to directly work with the underlying geospatial data and tools. The corresponding map data must be registered with cartographer either by the user or by another package.

This package provides functions for survey data including svydesign objects from the survey package that call ggplot2 to make bar charts, histograms, boxplots, and hexplots of survey data.

Facilitates the post-Genome Wide Association Studies (GWAS) and Quantitative Trait Loci (QTL) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) <doi:10.1038/s41598-024-66903-3>. It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output.

Identifies gendered words and phrases in text using a built in dictionary of more than two hundred gendered terms paired with gender neutral alternatives. Reports the share of gendered language in a text, lists every gendered term found together with its suggested neutral replacement, and can rewrite a text in gender neutral form. Plain text files are read with base R, while other document formats such as PDF and Word are supported through the optional readtext package. The dictionary is informed by published guidance on gender inclusive language, including the United Nations guidelines <https://www.un.org/en/gender-inclusive-language/> and the European Parliament guidance on gender neutral language.

This package performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.

This package contains published data sets for global benthic d18O data for 0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.

Informal implementation of some algorithms from Graph Theory and Combinatorial Optimization which arise in the subject "Graphs and Network Optimization" from first course of the EUPLA degree of Data Engineering in Industrial Processes.

Sequential change-point tests, parameters estimation, and goodness-of-fit tests for generalized Ornstein-Uhlenbeck processes.

Create epicurves, epigantt charts, and diverging bar charts using ggplot2'. Prepare data for visualisation or other reporting for infectious disease surveillance and outbreak investigation (time series data). Includes tidy functions to solve date based transformations for common reporting tasks, like (A) seasonal date alignment for respiratory disease surveillance, (B) date-based case binning based on specified time intervals like isoweek, epiweek, month and more, (C) automated detection and marking of the new year based on the date/datetime axis of the ggplot2', (D) labelling of the last value of a time-series. An introduction on how to use epicurves can be found on the US CDC website (2012, <https://www.cdc.gov/training/quicklearns/epimode/index.html>).

Add glossaries to markdown and quarto documents by tagging individual words. Definitions can be provided inline or in a separate file.

GitHub apps provide a powerful way to manage fine grained programmatic access to specific git repositories, without having to create dummy users, and which are safer than a personal access token for automated tasks. This package extends the gh package to let you authenticate and interact with GitHub <https://docs.github.com/en/rest/overview> in R as an app.

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with FAIR (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the DublinCore standard (<https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with ISO/TC211 (<https://committee.iso.org/home/tc211>) and OGC (<https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. PostGIS'), metadata catalogues (eg. GeoNetwork', CSW servers), data servers (eg. GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of EML metadata, and their management with DataOne servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of CF conventions, NetCDF data format and OPeNDAP access protocol, and their management with Thredds servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', DataCite'), to facilitate the publication of data within (meta)data repositories such as Zenodo (<https://zenodo.org>) or DataVerse (<https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference Zenodo DOI in GeoNetwork'/'GeoServer metadata, or vice versa reference GeoNetwork'/'GeoServer links in Zenodo or EML metadata). The use of standardized configuration files ('JSON or YAML formats) allow fully reproducible workflows to facilitate the work of data and information managers.

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.

Gradient-Enhanced Kriging as an emulator for computer experiments based on Maximum-Likelihood estimation.