This package creates diagrams with an object-oriented approach. Geometric objects have computed properties with information about themselves (e.g., their area) or about their relationships with other objects (e.g, the distance between their edges). The objects have methods to convert them to geoms that can be plotted in ggplot2'.

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

Run a Gibbs sampler for a multivariate Bayesian sparse group selection model with Dirac, continuous and hierarchical spike prior for detecting pleiotropy on the traits. This package is designed for summary statistics containing estimated regression coefficients and its estimated covariance matrix. The methodology is available from: Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021) <doi:10.1002/sim.8855>.

This package performs test procedures for general hypothesis testing problems for four multivariate coefficients of variation (Ditzhaus and Smaga, 2023 <arXiv:2301.12009>). We can verify the global hypothesis about equality as well as the particular hypotheses defined by contrasts, e.g., we can conduct post hoc tests. We also provide the simultaneous confidence intervals for contrasts.

Retrieving regional plant checklists, species traits and distributions, and environmental data from the Global Inventory of Floras and Traits (GIFT). More information about the GIFT database can be found at <https://gift.uni-goettingen.de/about> and the map of available floras can be visualized at <https://gift.uni-goettingen.de/map>. The API and associated queries can be accessed according the following scheme: <https://gift.uni-goettingen.de/api/extended/index2.0.php?query=env_raster>.

This package provides a collection of tools to create, use and maintain modularized model code written in the modeling language GAMS (<https://www.gams.com/>). Out-of-the-box GAMS does not come with support for modularized model code. This package provides the tools necessary to convert a standard GAMS model to a modularized one by introducing a modularized code structure together with a naming convention which emulates local environments. In addition, this package provides tools to monitor the compliance of the model code with modular coding guidelines.

This package provides a sparklyr <https://spark.rstudio.com/> extension that provides an R interface for GraphFrames <https://graphframes.github.io/>. GraphFrames is a package for Apache Spark that provides a DataFrame-based API for working with graphs. Functionality includes motif finding and common graph algorithms, such as PageRank and Breadth-first search.

This package implements LASSO regression using gradient descent with support for Gaussian, Binomial, Negative Binomial, and Zero-Inflated Negative Binomial (ZINB) families. Features cross-validation for determining lambda, stability selection, and bootstrapping for confidence intervals. Methods described in Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x> and Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.

Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. gambin is short for gamma-binomial'. The main function is fit_abundances(), which estimates the alpha parameter(s) of the gambin distribution using maximum likelihood. Functions are also provided to generate the gambin distribution and for calculating likelihood statistics.

Gitea is a community managed, lightweight code hosting solution were projects and their respective git repositories can be managed <https://gitea.io>. This package gives an interface to the Gitea API to access and manage repositories, issues and organizations directly in R.

Works with ggplot2 to add a Van Gogh color palette to the userâ s repertoire. It also has a function that work alongside ggplot2 to create more interesting data visualizations and add contextual information to the userâ s plots.

Duct tape the quanteda ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of quanteda.textmodels and provides a function to setup the Python environment to use the pretrained models from Hugging Face <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

This package provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.

This package provides an extension to ggplot2 (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of continuous confidence interval plots (Violin CI and Gradient CI plots), typically for the sample mean. These plots contain multiple user-defined confidence areas with varying colours, defined by the underlying t-distribution used to compute standard confidence intervals for the mean of the normal distribution when the variance is unknown. Two types of plots are available, a gradient plot with rectangular areas, and a violin plot where the shape (horizontal width) is defined by the probability density function of the t-distribution. These visualizations are studied in (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) <doi:10.1109/TVCG.2021.3073466>.

Generate multiple data sets for educational purposes to demonstrate the importance of multiple regression. The genset function generates a data set from an initial data set to have the same summary statistics (mean, median, and standard deviation) but opposing regression results.

This package implements the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Cucci, D. A., Voirol, L., Kermarrec, G., Montillet, J. P., and Guerrier, S. (2023) <doi:10.1007/s00190-023-01702-8>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals. The gmwmx package provides functions to estimate, compare and analyze models, utilities to load and work with Global Navigation Satellite System (GNSS) data as well as methods to compare results with the Maximum Likelihood Estimator (MLE) implemented in Hector.

After being given the location of your students submissions and a test file, the function runs each file that is an R script, R Markdown file, or Quarto document, and evaluates the results from all the given tests. Results are neatly returned in a data frame that has a row for each student, and a column for each test.

This package provides a collection of methods to determine growth rates from experimental data, in particular from batch experiments and plate reader trials.

This package provides a theme, a discrete color palette, and continuous scales to make ggplot2 look like gnuplot'. This may be helpful if you use both ggplot2 and gnuplot in one project.

Integer programming models to assign students to groups by maximising diversity within groups, or by maximising preference scores for topics.

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

This package provides a procedure that uses target-decoy competition (or knockoffs) to reject multiple hypotheses in the presence of group structure. The procedure controls the false discovery rate (FDR) at a user-specified threshold.

Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).