graphics, grDevices, igraph, Matrix.utils, parallel, Rcpp (>=
1.0.5), sccore, stats
C++11, GNU make (optional), libxml2 (optional),
glpk (>= 4.57, optional)

graphics, grDevices, igraph, parallel, Rcpp (>= 1.0.5),
sccore, stats

R and C/C++ Wrappers to Run the Leiden find_partition() Function
c( person('Brent', 'Ewing', email = 'bge@uw.edu', role = c('aut', 'cre')),
person('Vincent', 'Traag', role = 'ctb'),
person('Gábor', 'Csárdi', role = 'ctb'),
person('Tamás', 'Nepusz', role = 'ctb'),
person('Szabolcs', 'Horvat', role = 'ctb'),
person('Fabio', 'Zanini', role = 'ctb'))
An R to C/C++ interface that runs the Leiden community
detection algorithm to find a basic partition (). It runs the
equivalent of the 'leidenalg' find_partition() function, which is
given in the 'leidenalg' distribution file
'leiden/src/functions.py'. This package includes the
required source code files from the official 'leidenalg'
distribution and functions from the R 'igraph'
package.  The 'leidenalg' distribution is available from
<https://github.com/vtraag/leidenalg/>
and the R 'igraph' package is available from
<https://igraph.org/r/>.
The Leiden algorithm is described in the article by
Traag et al. (2019) <doi:10.1038/s41598-019-41695-z>.
Brent Ewing [aut, cre],
Vincent Traag [ctb],
Gábor Csárdi [ctb],
Tamás Nepusz [ctb],
Szabolcs Horvat [ctb],
Fabio Zanini [ctb]

Estimate the slope and intercept of a bivariate linear
relationship by calculating a posterior density that is
invariant to interchange and scaling of the coordinates.

c(person("David", "Leonard", role = c("aut", "cre"), email =
"david.leonard@utsouthwestern.edu"))

c(person("David", "Leonard", role = c("aut", "cre"),
email = "davidsleonard@outlook.com"))
Estimate the slope and intercept of a bivariate
linear relationship by calculating a posterior density
that is invariant to interchange and scaling of the
coordinates.

Laplace approximated EM Microarray Analysis
LEMMA is used to detect "nonnull genes" - genes for which
the average response in treatment group 1 is significantly
different from the average response in group 2, in normalized
microarray data. LEMMA is an implementation of an approximate
EM algorithm to estimate the parameters in the assumed linear
model in Bar, Booth, Schifano, Wells (2009).