This package provides a tool which allows users the ability to intuitively create flexible, reproducible portable document format reports comprised of aesthetically pleasing tables, images, plots and/or text.

This package provides convenient access to the official spatial datasets of Peru as sf objects in R. This package includes a wide range of geospatial data covering various aspects of Peruvian geography, such as: administrative divisions (Source: INEI <https://ide.inei.gob.pe/>), protected natural areas (Source: GEO ANP - SERNANP <https://geo.sernanp.gob.pe/visorsernanp/>). All datasets are harmonized in terms of attributes, projection, and topology, ensuring consistency and ease of use for spatial analysis and visualization.

This package provides a genetic algorithm framework for regression problems requiring discrete optimization over model spaces with unknown or varying dimension, where gradient-based methods and exhaustive enumeration are impractical. Uses a compact chromosome representation for tasks including spline knot placement and best-subset variable selection, with constraint-preserving crossover and mutation, exact uniform initialization under spacing constraints, steady-state replacement, and optional island-model parallelization from Lu, Lund, and Lee (2010, <doi:10.1214/09-AOAS289>). The computation is built on the GA engine of Scrucca (2017, <doi:10.32614/RJ-2017-008>) and changepointGA engine from Li and Lu (2024, <doi:10.48550/arXiv.2410.15571>). In challenging high-dimensional settings, GAReg enables efficient search and delivers near-optimal solutions when alternative algorithms are not well-justified.

This package provides R bindings to the GGML tensor library for efficient machine learning computation. Implements core tensor operations including element-wise arithmetic, reshaping, and matrix multiplication. Supports neural network layers (attention, convolutions, normalization), activation functions, and quantization. Features optimization/training API with AdamW (Adam with Weight decay) and SGD (Stochastic Gradient Descent) optimizers, MSE (Mean Squared Error) and cross-entropy losses. Multi-backend support with CPU and optional Vulkan GPU (Graphics Processing Unit) acceleration. See <https://github.com/ggml-org/ggml> for more information about the underlying library.

Reference datasets commonly used in the geosciences. These include standard atomic weights of the elements, a periodic table, a list of minerals including their abbreviations and chemistry, geochemical data of reservoirs (primitive mantle, continental crust, mantle, basalts, etc.), decay constants and isotopic ratios frequently used in geochronology, color codes of the chronostratigraphic chart. In addition, the package provides functions for basic queries of atomic weights, the list of minerals, and chronostratigraphic chart colors. All datasets are fully referenced, and a BibTeX file containing the references is included.

We implemented multiple tests based on the restricted mean survival time (RMST) for general factorial designs as described in Munko et al. (2024) <doi:10.1002/sim.10017>. Therefore, an asymptotic test, a groupwise bootstrap test, and a permutation test are incorporated with a Wald-type test statistic. The asymptotic and groupwise bootstrap test take the asymptotic exact dependence structure of the test statistics into account to gain more power. Furthermore, confidence intervals for RMST contrasts can be calculated and plotted and a stepwise extension that can improve the power of the multiple tests is available.

Create and maintain delayed-data packages (ddp's). Data stored in a ddp are available on demand, but do not take up memory until requested. You attach a ddp with g.data.attach(), then read from it and assign to it in a manner similar to S-PLUS, except that you must run g.data.save() to actually commit to disk.

This package contains five functions performing the calculation of unconditional and conditional Granger-causality spectra, bootstrap inference on both, and inference on the difference between them via the bootstrap approach of Farne and Montanari, 2018 <arXiv:1803.00374>.

This package provides tools to interact nicely with the Genius API <https://docs.genius.com/>. Search hosted content, extract associated metadata and retrieve lyrics with ease.

Derivative Free Gradient Projection Algorithms for Factor Rotation. For more details see ?GPArotateDF. Theory for these functions can be found in the following publications: Jennrich (2004) <doi:10.1007/BF02295647>. Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

This package provides extensions for various geographic spatial file formats, such as shape files and rasters. Currently provides support for the terra geographic spatial formats. See the vignettes for worked examples, demonstrations, and explanations of how to use the various package extensions.

An update to the Joint Location-Scale (JLS) testing framework that identifies associated SNPs, gene-sets and pathways with main and/or interaction effects on quantitative traits (Soave et al., 2015; <doi:10.1016/j.ajhg.2015.05.015>). The JLS method simultaneously tests the null hypothesis of equal mean and equal variance across genotypes, by aggregating association evidence from the individual location/mean-only and scale/variance-only tests using Fisher's method. The generalized joint location-scale (gJLS) framework has been developed to deal specifically with sample correlation and group uncertainty (Soave and Sun, 2017; <doi:10.1111/biom.12651>). The current release: gJLS2, include additional functionalities that enable analyses of X-chromosome genotype data through novel methods for location (Chen et al., 2021; <doi:10.1002/gepi.22422>) and scale (Deng et al., 2019; <doi:10.1002/gepi.22247>).

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>.

Collection of functions to enhance ggplot2 and ggiraph'. Provides functions for exploratory plots. All plot can be a static plot or an interactive plot using ggiraph'.

Perform association tests using generalized linear mixed models (GLMMs) in genome-wide association studies (GWAS) and sequencing association studies. First, GMMAT fits a GLMM with covariate adjustment and random effects to account for population structure and familial or cryptic relatedness. For GWAS, GMMAT performs score tests for each genetic variant as proposed in Chen et al. (2016) <DOI:10.1016/j.ajhg.2016.02.012>. For candidate gene studies, GMMAT can also perform Wald tests to get the effect size estimate for each genetic variant. For rare variant analysis from sequencing association studies, GMMAT performs the variant Set Mixed Model Association Tests (SMMAT) as proposed in Chen et al. (2019) <DOI:10.1016/j.ajhg.2018.12.012>, including the burden test, the sequence kernel association test (SKAT), SKAT-O and an efficient hybrid test of the burden test and SKAT, based on user-defined variant sets.

This package provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.

Palettes based on video games.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators.

On Galaxy platforms like Galaxy Europe <https://usegalaxy.eu>, many tools and workflows can run directly on a high-performance computer. GalaxyR connects R with Galaxy platforms API <https://usegalaxy.eu/api/docs> and allows credential management, uploading data, invoking workflows or tools, checking their status, and downloading results.

This package provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators. H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.

This package provides a collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.

Genomic biology is not limited to the confines of the canonical B-forming DNA duplex, but includes over ten different types of other secondary structures that are collectively termed non-B DNA structures. Of these non-B DNA structures, the G-quadruplexes are highly stable four-stranded structures that are recognized by distinct subsets of nuclear factors. This package provide functions for predicting intramolecular G quadruplexes. In addition, functions for predicting other intramolecular nonB DNA structures are included.

This package purposes to deal with public survey data of Japanese government via their Application Programming Interface (http://statdb.nstac.go.jp/).