This package provides a simple, opinionated toolkit for visualizing genomic variant data using a ggplot2'-native grammar. Accepts VCF files or plain data frames and produces publication-ready lollipop plots, consequence summaries, mutational spectrum charts, and cohort-level comparisons with minimal code. Designed for both wet-lab biologists and experienced bioinformaticians.

Fits gastric emptying time series from MRI or scintigraphic measurements using nonlinear mixed-model population fits with nlme and Bayesian methods with Stan; computes derived parameters such as t50 and AUC.

This package provides methods for dividing data into groups. Create balanced partitions and cross-validation folds. Perform time series windowing and general grouping and splitting of data. Balance existing groups with up- and downsampling or collapse them to fewer groups.

Extensions to ggplot2 providing low-level debug tools: statistics and geometries echoing their data argument. Layer manipulation: deletion, insertion, extraction and reordering of layers. Deletion of unused variables from the data object embedded in "ggplot" objects.

Download and process public domain works in the Project Gutenberg collection <https://www.gutenberg.org/>. Includes metadata for all Project Gutenberg works, so that they can be searched and retrieved.

Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.

Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,â ¦,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.

Streamlines downloading and cleaning biodiversity data from Integrated Digitized Biocollections (iDigBio) and the Global Biodiversity Information Facility (GBIF).

Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.

This package provides a new take on the bar chart. Similar to a waffle style chart but instead of squares the layout resembles a brick wall.

Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids. Two main functions: Run_Clustering() that regroups individuals with a same genotype based on proximity and Run_Genotyping() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().

Connects to the Google Charts geographic data resources described in <https://developers.google.com/chart/interactive/docs/gallery/geochart>, allowing the user to download contents to use as a reference for related services like Google Trends'.

Methodology that combines feature selection, model tuning, and parsimonious model selection with Genetic Algorithms (GA) proposed in Martinez-de-Pison (2015) <DOI:10.1016/j.asoc.2015.06.012>. To this objective, a novel GA selection procedure is introduced based on separate cost and complexity evaluations.

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.

Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroscedastic latent variables.

This package provides a collection of functions to set up Google Public Data Explorer <https://www.google.com/publicdata/> data visualization tool with your own data, building automatically the corresponding DataSet Publishing Language file, or DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to be published in Public Data Explorer'.

Genomic signatures represent unique features within a species DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) <doi:10.1016/s0378-1119(03)00581-x> and Xiao et al. (2015) <doi:10.1093/bioinformatics/btv042>. The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) <doi:10.1186/1471-2105-9-48>. The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) <doi:10.1093/oxfordjournals.molbev.a026048> and Jeffrey et al. (1990) <doi:10.1093/nar/18.8.2163>. The GC content is calculated based on the work published by Nakabachi et al. (2006) <doi:10.1126/science.1134196> and Barbu et al. (1956) <https://pubmed.ncbi.nlm.nih.gov/13363015>. The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) <doi:10.1186/1471-2164-10-S3-S10>. The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) <https://urn.nsk.hr/urn:nbn:hr:217:686131>.

Supplies a set of functions to interface with bikeshare data following the General Bikeshare Feed Specification, allowing users to query and accumulate tidy datasets for specified cities/bikeshare programs.

Wrappers for functions in the gRain package to emulate some RHugin functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both hard (boolean) or soft (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with RHugin', but this package should assist users without access to Hugin to use code written to use RHugin'.

Group Sequential Operating Characteristics for Clinical, Bayesian two-arm Trials with known Sigma and Normal Endpoints, as described in Gerber and Gsponer (2016) <doi: 10.18637/jss.v069.i11>.

This package provides functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.

Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study. Methods included are ANOVA and Average / Range methods. Requires balanced study.

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'.

Simulating composite endpoints with recurrent and terminal events under staggered entry, and for constructing one- and two-sample group sequential test statistics and monitoring boundaries based on the mean frequency function. Details will be available in an upcoming publication.