worth fixing.  These FITPACK routines are over 20 years old and
may be obsolete.  If you want to fix it, I will happily make
Depends: R (>= 2.4.0), stats, lattice, PolynomF, fda

This package provides a wrapper to the FITPACK routines written by
Paul Dierckx. The original Fortran is available from
Currently, it seems NOT to have any users and is not worth fixing.
These FITPACK routines are over 20 years old and may be obsolete.
If you want to fix it, I will happily make you the maintainer.

Title: Difference of Two Precision Matrices Estimation
Description: Difference of two precision matrices is estimated by the d-trace loss with lasso penalty, given two sample classes.

Title: Analyzing and Visualizing Differential Correlation Networks in
Depends: pcaMethods, igraph, fdrtool, multtest
Description: A method for identifying pattern changes between 2 experimental
conditions in correlation networks (e.g., gene co-expression networks),
which builds on a commonly used association measure, such as Pearson's
correlation coefficient. This package includes functions to calculate
correlation matrices for high-dimensional dataset and to test
differential correlation, which means the changes in the correlation
relationship among variables (e.g., genes and metabolites) between 2

Authors@R: c(person("Atsushi", "Fukushima", role = c("aut", "cre"),

Title: Detection of Statistically Significant Changes in Complex
Author: Raghvendra Mall [aut, cre], Khalid B. Kunji [aut]
Authors@R: c(person(given = "Raghvendra", family = "Mall", role = c("aut",
"cre"), email = "rmall@hbku.edu.qa"), person(given = "Khalid B.",
family = "Kunji", role = c("aut"), email = "kkunji@hbku.edu.qa"))
Description: Provides an implementation of statistically significant
differential sub-network analysis for paired biological networks.
Imports: Rcpp (>= 0.12.7), Matrix, qlcMatrix, data.table, Hmisc,
gplots, igraph, lsa, foreach, doParallel

Title: Distributional Differences Across Contingency Tables
Description: Statistical methods for hypothesis testing of differences in the
underlying distributions across two or more contingency tables. The package
includes three tests: the comparative chi-squared test (Song et al, 2014)
Sharma-Song test, and the heterogeneity test. They all have an
asymptotically chi-squared null distribution. These options allow one to
test for patterns that differ in first and second orders, directly related
to marginal and joint distributions of given contingency tables.
Suggests: Ckmeans.1d.dp, FunChisq, knitr, rmarkdown, testthat