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'.

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).

This package provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) <doi:10.1089/jir.2017.0127>.

Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.

Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>). Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.

This package provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

This package provides a pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) <doi:10.1101/436659> for more information and examples.

Analytics to read in and segment raw GENEActiv accelerometer data into epochs and events. For more details on the GENEActiv device, see <https://activinsights.com/resources/geneactiv-support-1-2/>.

This package provides methods include converting series of event names to strings, finding common patterns in a group of strings, discovering "unique" patterns when comparing two groups of strings as well as the number and starting position of each pattern in each string, obtaining transition matrix, computing transition entropy, statistically comparing the difference between two groups of strings, and clustering string groups. Event names can be any action names or labels such as events in log files or areas of interest (AOIs) in eye tracking research. An R Shiny application is available on GitHub.

This package provides tools to measure the reliability of an Information Retrieval test collection. It allows users to estimate reliability using Generalizability Theory and map those estimates onto well-known indicators such as Kendall tau correlation or sensitivity.

This package provides systematic, dependency-aware exploration of group sequential designs created with gsDesign'. Supports reproducible grid and random search over user-defined candidate sets, parallel evaluation via the future framework, standardized metric extraction, and auditable reporting for design-space evaluation and trade-off analysis. Methods for group sequential design are described in Anderson (2025) <doi:10.32614/CRAN.package.gsDesign>. The future framework for parallel processing is described in Bengtsson (2021) <doi:10.32614/RJ-2021-048>.

An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version provides two optimization methods: Bayesian optimization and random search. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.

Computes the gravitational and magnetic anomalies generated by 3-D vertical rectangular prisms at specific observation points using the method of Plouff (1976) <doi:10.1190/1.1440645>.

This package provides a quantile-quantile plot can be used to compare a sample of p-values to the uniform distribution. But when the dataset is big (i.e. > 1e4 p-values), plotting the quantile-quantile plot can be slow. geom_QQ uses all the data to calculate the quantiles, but thins it out in a way that focuses on points near zero before plotting to speed up plotting and decrease file size, when vector graphics are stored.

This package provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

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.

This package provides a set of high efficient functions to decode identifiers of National Football League players.

This package provides functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.

Implement a coherent and flexible protocol for animal color tagging. GenTag provides a simple computational routine with low CPU usage to create color sequences for animal tag. First, a single-color tag sequence is created from an algorithm selected by the user, followed by verification of the combination uniqueness. Three methods to produce color tag sequences are provided. Users can modify the main function core to allow a wide range of applications.

This function is an extension of the Small Area Estimation (SAE) model. Geoadditive Small Area Model is a combination of the geoadditive model with the Small Area Estimation (SAE) model, by adding geospatial information to the SAE model. This package refers to J.N.K Rao and Isabel Molina (2015, ISBN: 978-1-118-73578-7), Bocci, C., & Petrucci, A. (2016)<doi:10.1002/9781118814963.ch13>, and Ardiansyah, M., Djuraidah, A., & Kurnia, A. (2018)<doi:10.21082/jpptp.v2n2.2018.p101-110>.

Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by the sf package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.

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.

The aim of this package is to offer more variability of graphics based on the self-organizing maps.

This package infers state-recorded gender categories from first names and dates of birth using historical datasets. By using these datasets instead of lists of male and female names, this package is able to more accurately infer the gender of a name, and it is able to report the probability that a name was male or female. GUIDELINES: This method must be used cautiously and responsibly. Please be sure to see the guidelines and warnings about usage in the README or the package documentation. See Blevins and Mullen (2015) <http://www.digitalhumanities.org/dhq/vol/9/3/000223/000223.html>.