Generate commonly used plots in the field of design of experiments using ggplot2'. ggDoE currently supports the following plots: alias matrix, box cox transformation, boxplots, lambda plot, regression diagnostic plots, half normal plots, main and interaction effect plots for factorial designs, contour plots for response surface methodology, Pareto plot, and two dimensional projections of a latin hypercube design.

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.

Make efficient Rust implementations of graph adjustment identification distances available in R. These distances (based on ancestor, optimal, and parent adjustment) count how often the respective adjustment identification strategy leads to causal inferences that are incorrect relative to a ground-truth graph when applied to a candidate graph instead. See also Henckel, Würtzen, Weichwald (2024) <doi:10.48550/arXiv.2402.08616>.

The ggplot2 package provides simple functions for visualizing contours of 2-d kernel density estimates. ggdensity implements several additional density estimators as well as more interpretable visualizations based on highest density regions instead of the traditional height of the estimated density surface.

Extends classical linear and quadratic discriminant analysis by incorporating permutation group symmetries into covariance matrix estimation. The package leverages methodology from the gips framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about gips methodology see and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, KoÅ odziejek (2025, <doi:10.18637/jss.v112.i07>).

This package performs genomic mediation analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/gr.216754.116>. It implements large scale mediation analysis and adaptively selects potential confounding variables to adjust for each mediation test from a pool of candidate confounders. The package is tailored for but not limited to genomic mediation analysis (e.g., cis-gene mediating trans-gene regulation pattern where an eQTL, its cis-linking gene transcript, and its trans-gene transcript play the roles as treatment, mediator and the outcome, respectively), restricting to scenarios with the presence of cis-association (i.e., treatment-mediator association) and random eQTL (i.e., treatment).

Método simples e eficiente de geolocalizar dados no Brasil. O pacote é baseado em conjuntos de dados espaciais abertos de endereços brasileiros, utilizando como fonte principal o Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE). O CNEFE é publicado pelo Instituto Brasileiro de Geografia e Estatà stica (IBGE), órgão oficial de estatà sticas e geografia do Brasil. (A simple and efficient method for geolocating data in Brazil. The package is based on open spatial datasets of Brazilian addresses, primarily using the Cadastro Nacional de Endereços para Fins Estatà sticos (CNEFE), published by the Instituto Brasileiro de Geografia e Estatà stica (IBGE), Brazil's official statistics and geography agency.).

This package provides methods for fitting macroevolutionary models to phylogenetic trees Pennell (2014) <doi:10.1093/bioinformatics/btu181>.

This package provides a function built on ggplot2 that visualizes pairwise BLAST alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.

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 package provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.

This package provides a wrapper of different standard estimation methods for gravity models. This package provides estimation methods for log-log models and multiplicative models.

Use GTFS (General Transit Feed Specification) data for routing from nominated start and end stations, for extracting isochrones', and travel times from any nominated start station to all other stations.

Description: For the risk, progression, and response to treatment of many complex diseases, it has been increasingly recognized that gene-environment interactions play important roles beyond the main genetic and environmental effects. In practical interaction analyses, outliers in response variables and covariates are not uncommon. In addition, missingness in environmental factors is routinely encountered in epidemiological studies. The developed package consists of five robust approaches to address the outliers problems, among which two approaches can also accommodate missingness in environmental factors. Both continuous and right censored responses are considered. The proposed approaches are based on penalization and sparse boosting techniques for identifying important interactions, which are realized using efficient algorithms. Beyond the gene-environment analysis, the developed package can also be adopted to conduct analysis on interactions between other types of low-dimensional and high-dimensional data. (Mengyun Wu et al (2017), <doi:10.1080/00949655.2018.1523411>; Mengyun Wu et al (2017), <doi:10.1002/gepi.22055>; Yaqing Xu et al (2018), <doi:10.1080/00949655.2018.1523411>; Yaqing Xu et al (2019), <doi:10.1016/j.ygeno.2018.07.006>; Mengyun Wu et al (2021), <doi:10.1093/bioinformatics/btab318>).

This package provides methods include converting series of event names to strings, finding common patterns in a group of strings, discovering featured 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.

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.

This package provides a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.

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>.

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.

Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.

This package provides functions to identify European NUTS (Nomenclature of Territorial Units for Statistics) regions for geographic coordinates (latitude/longitude) using Eurostat geospatial boundaries. Includes map-based visualisation of the matched regions for validation and exploration. Designed for regional data analysis, reproducible workflows, and integration with common geospatial R packages.

Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.

Process in-situ Gamma-Ray Spectrometry for Luminescence Dating. This package allows to import, inspect and correct the energy shifts of gamma-ray spectra. It provides methods for estimating the gamma dose rate by the use of a calibration curve as described in Mercier and Falguères (2007). The package only supports Canberra CNF and TKA and Kromek SPE files.