Bayesian Summary Data Models for Mendelian Randomization Studies
c(person("Okezie", "Uche-Ikonne",
email = "o.uche-ikonne@lancaster.ac.uk",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8461-8099")),
person("Frank", "Dondelinger",
email = "f.dondelinger@lancaster.ac.uk", role = c("aut"),
comment = c(ORCID = "0000-0003-1816-6300")),
person("Tom", "Palmer",
email = "tom.palmer@lancaster.ac.uk", role = c("aut"),
comment = c(ORCID = "0000-0003-4655-4511")))
Bayesian estimation of inverse variance weighted (IVW), Burgess
et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et
al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian
randomization analyses.
testthat, rjags (>= 4-9), tidyverse, knitr, ggplot2, ggmcmc,
foreign, DescTools, bayesmeta, utils, kableExtra, roxygen2,
bookdown

c(
person("Okezie", "Uche-Ikonne",
email = "o.uche-ikonne@lancaster.ac.uk",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8461-8099")),
person("Frank", "Dondelinger",
email = "f.dondelinger@lancaster.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0003-1816-6300")),
person("Tom", "Palmer",
email = "tom.palmer@bristol.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0003-4655-4511")))

testthat, rjags (>= 4-9), tidyverse, knitr, ggplot2, ggmcmc,
foreign, DescTools, bayesmeta, utils, kableExtra, roxygen2,
bookdown, MendelianRandomization

c(
person("Okezie", "Uche-Ikonne",
email = "okezieikonne@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8461-8099")),
person("Frank", "Dondelinger",
email = "f.dondelinger@lancaster.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0003-1816-6300")),
person("Tom", "Palmer",
email = "tom.palmer@bristol.ac.uk",
role = c("aut"),
comment = c(ORCID = "0000-0003-4655-4511")))

Magnetic Resonance Binning, Integration and Normalization
person(given = "Matthias",
family = "Klein",
role = c("aut", "cre"),
email = "klein.663@osu.edu",
comment = c(ORCID = "0000-0001-7455-5381"))
Nuclear Magnetic Resonance is widely used in Life Science
research. The package (<http://www.kleinomicslab.com/software>) converts 1D or 2D data into a matrix of values
suitable for further data analysis and performs basic processing steps in a
reproducible way. Negative values, a common issue in such data, are replaced
by positive values. All used parameters are stored in a readable text file
and can be restored from that file to enable exact reproduction of the data
at a later time.
Matthias Klein [aut, cre] (<https://orcid.org/0000-0001-7455-5381>)