Title: Fetching Tabular Data "Onload" in Compiled R Markdown HTML
Description: Dynamically retrieve data from the web to render HTML tables
on inspection in R Markdown HTML documents.
Author: Yongfu Liao [aut, cre] (<https://orcid.org/0000-0002-1814-2993>)

Title: Access to Ethereum-Based Blockchains Through Geth Nodes
Authors@R: person("Vicente", "Garcia Diaz", email = "garciavicente@uniovi.es", role = c("aut", "cre"))
Description: Full access to the Geth command line interface for running full Ethereum nodes. With
gethr it is possible to carry out different tasks such as mine ether, transfer funds, create
contacts, explore block history, etc. The package also provides access to all the available APIs.
The officially exposed by Ethereum blockchains (eth, shh, web3, net) and some provided directly
by Geth (admin, debug, miner, personal, txpool). For more details on Ethereum, access the project
website <https://www.ethereum.org/>. For more details on the Geth client, access the project
Maintainer: Vicente Garcia Diaz <garciavicente@uniovi.es>

Description: A micro-package for getting your 'IP' address, either the
local/internal or the public/external one. Currently only 'IPv4' addresses
Authors@R: c(person("Drew", "Schmidt", role=c("aut", "cre"), email="wrathematics@gmail.com"),

Title: Get Landsat 8 Data from Amazon Public Data Sets
Description: Get Landsat 8 Data from Amazon Web Services ('AWS')
Includes functions for listing images and fetching them, and handles
caching to prevent unnecessary additional requests.
Suggests: roxygen2 (>= 5.0.1), testthat, knitr, covr, raster

email = "myrmecocystus@gmail.com", comment = c(ORCID="0000-0003-1444-9135")))
Suggests: roxygen2 (>= 6.0.1), testthat, knitr, raster
X-schema.org-keywords: landsat, nasa, aws, image, imaging, usgs, earth

Title: Quantifying Systematic Heterogeneity in Meta-Analysis
person("Lerato E", "Magosi", email = "lmagosi@well.ox.ac.uk",
person("Jemma C", "Hopewell", email = "jemma.hopewell@ndph.ox.ac.uk",
person("Martin", "Farrall", email = "mfarrall@well.ox.ac.uk",
Description: Quantifying systematic heterogeneity in meta-analysis using R.
The \code{M} statistic aggregates heterogeneity information across multiple
variants to, identify systematic heterogeneity patterns and their direction
of effect in meta-analysis. It's primary use is to identify outlier studies,
which either show "null" effects or consistently show stronger or weaker
genetic effects than average across, the panel of variants examined in a
GWAS meta-analysis. In contrast to conventional heterogeneity metrics
(Q-statistic, I-squared and tau-squared) which measure random heterogeneity
at individual variants, \code{M} measures systematic (non-random)
heterogeneity across multiple independently associated variants. Systematic
heterogeneity can arise in a meta-analysis due to differences in the study
characteristics of participating studies. Some of the differences may
include: ancestry, allele frequencies, phenotype definition, age-of-disease
onset, family-history, gender, linkage disequilibrium and quality control
thresholds. See <https://magosil86.github.io/getmstatistic/> for statistical
statistical theory, documentation and examples.
Maintainer: Lerato E Magosi <lmagosi@well.ox.ac.uk>