KernSmooth, rgl (>= 0.79), vcd, xlsReadWrite
Provides a GUI with which users can construct and interact
with biplots.

Anthony la Grange, with some Fortran code adapted by NJ le Roux
from the original by PJ Rousseeuw, I Ruts and JW Tukey

person(given = "Jose Giovany",
family = "Babativa-Marquez",
role = c("cre", "aut"),
email = "gbabativam@gmail.com",
comment = c(ORCID = "0000-0002-4989-7459"))
Logistic Biplot is a method that allows representing multivariate binary data on a subspace of low dimension, where each individual is represented by a point and each variable as vectors directed through the origin. The orthogonal projection of individuals onto these vectors predicts the expected probability that the characteristic occurs. The package contains new techniques to estimate the model parameters and constructs in each case the 'Logistic-Biplot'. References can be found in the help of each procedure.
testthat, knitr, rmarkdown, RSpectra, dplyr (>= 1.0.0), tidyr
(>= 1.1.0), ggplot2 (>= 3.3.2), ggrepel, pracma, mvtnorm
Jose Giovany Babativa-Marquez [cre, aut]
(<https://orcid.org/0000-0002-4989-7459>)

Bisulfite-treated RNA non-conversion in a set of samples is analysed as
follows: each sample's Poisson parameter is estimated, and non-conversion
p-values are calculated for each sample and adjusted for multiple testing.
Finally, combined non-conversion p-value and standard error of the non-conversion
are calculated on the intersection of the set of samples.
A low combined non-conversion p-value points to methylation of the
corresponding RNA cytosine, or another event blocking bisulfite conversion.

Bisulfite-treated RNA non-conversion in a set of samples is
analysed as follows : each sample's non-conversion distribution is
identified to a Poisson distribution. P-values adjusted for multiple
testing are calculated in each sample. Combined non-conversion P-values
and standard errors are calculated on the intersection of the set of
samples. For further details, see C Legrand, F Tuorto, M Hartmann,
R Liebers, D Jakob, M Helm and F Lyko (2017) <doi:10.1101/gr.210666.116>.