Title: Implementation of 2D Correlation Analysis
person("Robert", "Geitner", email = "robert.geitner@uni-jena.de", role = c("cre","aut")),
person("Robby", "Fritzsch", role = "aut"),
person("Thomas", "Bocklitz", role = "aut"),
person("Juergen", "Popp", role = c("ctb","cph")))
Description: Implementation of two-dimensional (2D) correlation analysis based
on the Fourier-transformation approach described by Isao Noda (I. Noda
functions for the resulting correlation matrix: The first one creates
colored 2D plots, while the second one generates 3D plots.
Imports: doParallel (>= 1.0.8), foreach (>= 1.4.3), parallel (>=

Title: Implementation of 2D Correlation Analysis in R

coloured 2D plots, while the second one generates 3D plots.

person("Robert", "Geitner", email = "r.geitner@uu.nl", role = c("cre","aut")),

Suggests: knitr, rmarkdown, R.rsp, testthat

Suggests: knitr, rmarkdown, R.rsp, testthat, rgl (>= 0.93.996-1)

person("Robert", "Geitner", email = "robert.geitner@tu-ilmenau.de", role = c("cre","aut")),
Suggests: knitr, rmarkdown, R.rsp, testthat, rgl (>= 0.93.996-1),

Title: Finding Associations in Position-Wise Aligned DNA Sequence
Description: Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.

Title: Correlation Arrays and 2-Sample Correlation Matrices
Maintainer: Rohan Reddy Tummala <rohantummala@gmail.com>
Description: The goal of 'corrarray' is to create a multi-sample
correlation array by combining the correlation matrices of a data set
For two specified levels of the variable, 'corrarray' displays one level's
correlation matrix in the lower triangular matrix and the other level's
correlation matrix in the upper triangular matrix.
Such an output can enable visualization of correlations from two samples
in a single correlation matrix or corrgram.

Title: Corrected the Coverage of Credible Sets from Bayesian Genetic
person("Anna", "Hutchinson", role = c("aut", "cre"), email = "anna.hutchinson@mrc-bsu.cam.ac.uk"),
person("Chris", "Wallace", role = c("aut")),
person("Kevin", "Kunzmann", role = c("ctb"))
Description: Using a computationally efficient method, the package can
be used to find the corrected coverage estimate of a credible set
of putative causal variants from Bayesian genetic fine-mapping.
The package can also be used to obtain a corrected credible set
if required; that is, the smallest set of variants required such
that the corrected coverage estimate of the resultant credible set is
within some user defined accuracy of the desired coverage.
Fortune and Wallace (2018) <doi:10.1093/bioinformatics/bty898>.
Suggests: covr, dplyr, knitr, mvtnorm, rmarkdown, testthat, simGWAS,
Imports: data.table, magrittr, stats, matrixStats, Rcpp