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.

Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood (see Albert, C., Vogel, S. and Ashauer, R. (2016) <doi:10.1371/journal.pcbi.1004978>).

Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.

Several tools for Global Value Chain ('GVC') analysis are
    implemented.

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

The 'Geocoordinate Validation Service' (GVS) runs checks of coordinates in latitude/longitude format. It returns annotated coordinates with additional flags and metadata that can be used in data cleaning.  Additionally, the package has functions related to attribution and metadata information. More information can be found at <https://github.com/ojalaquellueva/gvs/tree/master/api>.

Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive 'Tableau'-like interface, leveraging the 'graphic-walker' JavaScript library and the 'htmlwidgets' package.

Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using 'ggplot2'.

Provides a reproducible pipeline to conduct genome‑wide association studies
    (GWAS) and extract single‑nucleotide polymorphisms (SNPs) for a human trait or
    disease. Given aggregated GWAS dataset(s) and a user‑defined significance threshold,
    the package retrieves significant SNPs from the GWAS Catalog and the
    Experimental Factor Ontology (EFO), annotates their gene context, and can write a
    harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data
    (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered
    regularly interspaced short palindromic repeats (CRISPR) guide design.
    For details on the resources and methods see:
    Buniello et al. (2019) <doi:10.1093/nar/gky1120>;
    Sollis et al. (2023) <doi:10.1093/nar/gkac1010>;
    Jinek et al. (2012) <doi:10.1126/science.1225829>;
    Malone et al. (2010) <doi:10.1093/bioinformatics/btq099>;
    Experimental Factor Ontology (EFO) <https://www.ebi.ac.uk/efo>.