Genotype plus genotype-by-environment (GGE) biplots rendered using ggplot2'. Provides a command line interface to all of the functionality contained within the archived package GGEBiplotGUI'.

Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <doi:10.48550/arXiv.1902.06565>.

Data sets and scripts used in the book Generalized Additive Models: An Introduction with R', Wood (2006,2017) CRC.

Performing the different steps of gene set enrichment meta-analysis. It provides different functions that allow the application of meta-analysis based on the combination of effect sizes from different pathways in different studies to obtain significant pathways that are common to all of them.

When you prepare a presentation or a report, you often need to manage a large number of ggplot figures. You need to change the figure size, modify the title, label, themes, etc. It is inconvenient to go back to the original code to make these changes. This package provides a simple way to manage ggplot figures. You can easily add the figure to the database and update them later using CLI (command line interface) or GUI (graphical user interface).

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

This package provides a high performance interface to the Global Biodiversity Information Facility, GBIF'. In contrast to rgbif', which can access small subsets of GBIF data through web-based queries to a central server, gbifdb provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary SQL or dplyr operations on the complete GBIF data tables (now over 1 billion records, and over a terabyte in size). gbifdb accesses a copy of the GBIF data in parquet format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in <https://duckdb.org/why_duckdb> and <https://arrow.apache.org/docs/r/articles/fs.html> respectively.

This package creates presentation-ready tables summarizing data sets, regression models, and more. The code to create the tables is concise and highly customizable. Data frames can be summarized with any function, e.g. mean(), median(), even user-written functions. Regression models are summarized and include the reference rows for categorical variables. Common regression models, such as logistic regression and Cox proportional hazards regression, are automatically identified and the tables are pre-filled with appropriate column headers.

Visualise the results of F test to compare two variances, Student's t-test, test of equal or given proportions, Pearson's chi-squared test for count data and test for association/correlation between paired samples.

Generalized Turnbull's estimator proposed by Dehghan and Duchesne (2011).

API bindings to the Geospatial Data Abstraction Library ('GDAL', <https://gdal.org>). Implements the GDAL Raster and Vector Data Models. Bindings are implemented with Rcpp modules. Exposed C++ classes and stand-alone functions wrap much of the GDAL API and provide additional functionality. Calling signatures resemble the native C, C++ and Python APIs provided by the GDAL project. Class GDALRaster encapsulates a GDALDataset and its raster band objects. Class GDALVector encapsulates an OGRLayer and the GDALDataset that contains it. Initial bindings are provided to the unified gdal command line interface added in GDAL 3.11. C++ stand-alone functions provide bindings to most GDAL "traditional" raster and vector utilities, including OGR facilities for vector geoprocessing, several algorithms, as well as the Geometry API ('GEOS via GDAL headers), the Spatial Reference Systems API, and methods for coordinate transformation. Bindings to the Virtual Systems Interface ('VSI') API implement standard file system operations abstracted for URLs, cloud storage services, Zip'/'GZip'/'7z'/'RAR', in-memory files, as well as regular local file systems. This provides a single interface for operating on file system objects that works the same for any storage backend. A custom raster calculator evaluates a user-defined R expression on a layer or stack of layers, with pixel x/y available as variables in the expression. Raster combine() identifies and counts unique pixel combinations across multiple input layers, with optional raster output of the pixel-level combination IDs. Basic plotting capability is provided for raster and vector display. gdalraster leans toward minimalism and the use of simple, lightweight objects for holding raw data. Currently, only minimal S3 class interfaces have been implemented for selected R objects that contain spatial data. gdalraster may be useful in applications that need scalable, low-level I/O, or prefer a direct GDAL API.

Allows you to retrieve information from the Google Knowledge Graph API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The Knowledge Graph Search API lets you find entities in the Google Knowledge Graph'. The API uses standard schema.org types and is compliant with the JSON-LD specification.

Extends ggplot2 for visualizing interval-valued data with scatter plots, histograms, index plots, boxplots, radar plots, PCA displays, and correlation heatmaps. The package also converts classical data tables into interval-valued data using clustering algorithms or user-defined groupings.

This package provides a collection of several geoms to create graphics, using ggplot2 and the Cartesian coordinate system. You use the familiar mapping Grammar of Graphics without the need to do another transformation into polar coordinates.

Improved version of GRIN software that streamlines its use in practice to analyze genomic lesion data, accelerate its computing, and expand its analysis capabilities to answer additional scientific questions including a rigorous evaluation of the association of genomic lesions with RNA expression. Pounds, Stan, et al. (2013) <DOI:10.1093/bioinformatics/btt372>.

The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.

Add mean comparison annotations to a ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

Generates a file, containing the main scientific references, prepared to be automatically inserted into an academic paper. The articles present in the list are chosen from the main references generated, by function principal_lister(), of the package bibliorefer'. The generated file contains the list of metadata of the principal references in BibTex format. Massimo Aria, Corrado Cuccurullo. (2017) <doi:10.1016/j.joi.2017.08.007>. Caibo Zhou, Wenyan Song. (2021) <doi:10.1016/j.jclepro.2021.126943>. Hamid DerviÅ . (2019) <doi:10.5530/jscires.8.3.32>.

This package implements cost-benefit analysis primitives from HM Treasury Green Book guidance (HM Treasury, 2022, 2026): the kinked Social Time Preference Rate ('STPR'), discount factors, net present value ('NPV'), equivalent annual cost, and real-terms rebasing using the GDP deflator. Designed for UK central government appraisal and evaluation. Bundled parameter tables carry vintage metadata for reproducibility.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

This package provides a collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

This package provides a Humanitarian Data Exchange (HDX) theme, color palettes, and scales for ggplot2 to allow users to easily follow the HDX visual design guide, including convenience functions for for loading and using the Source Sans 3 font.

Ease the transition between R vectors and markdown text. With gluedown and rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) <https://spec.commonmark.org/>.