graphics, grDevices, maptools, PBSmapping, raster, sp,
spatstat.geom, spatstat.core, spatstat.linnet, spatstat (>=
2.0-0), spdep, stats, utils

graphics, grDevices, maptools, PBSmapping, raster, sp,
spatstat.geom, spatstat.linnet, spatstat (>= 2.0-0), spdep,
stats, utils

Keyan Salari, Robert Tibshirani, Jonathan R. Pollack
Integrative analysis of DNA copy number and gene
expression data described in Salari et al 2009

Discontinuous Regression and Image Processing
This is a collection of functions for discontinuous regression
analysis and image processing.
R (>= 2.10.0), readbitmap, caTools, graphics, stats

This is a collection of functions for discontinuous regression
analysis and image processing (DRIP). A recent addition is the
blind image deblurring via jump-preserving extrapolation.

R (>= 2.10.0), readbitmap, graphics, stats

This is a collection of functions for discontinuous regression
analysis and image processing (DRIP). A recent addition is the
blind image deblurring via jump-preserving extrapolation. This
version removes the dependence on the GNU Scientific Library.
R (>= 3.5.0), parallel, readbitmap, graphics, stats

This is a collection of functions for discontinuous regression
analysis and image processing (DRIP). This version removes the
dependence on the GNU Scientific Library.

person("Guido", "Kraemer",
email = "gkraemer@bgc-jena.mpg.de",
role = c("aut","cre"))
An Implementation of Dimensionality Reduction
via Regression using Kernel Ridge Regression.

person("Guido", "Kraemer", email = "gkraemer@bgc-jena.mpg.de", role = c("aut","cre"))

person("Charles", "Blatti", email = "blatti@illinois.edu", role = c("aut", "cre"))
We present DRaWR, a network-based method for ranking genes or
properties related to a given gene set. Such related genes or properties are
identified from among the nodes of a large, heterogeneous network of biological
information. Our method involves a random walk with restarts, performed on
an initial network with multiple node and edge types, preserving more of the
original, specific property information than current methods that operate
on homogeneous networks. In this first stage of our algorithm, we find the
properties that are the most relevant to the given gene set and extract a
subnetwork of the original network, comprising only the relevant properties. We
then rerank genes by their similarity to the given gene set, based on a second
random walk with restarts, performed on the above subnetwork.