Packaged: 2019-05-10 10:47:59 UTC; boulesteix

Author: Anne-Laure Boulesteix, Mathias Fuchs, Gerhard Schulze
Description: The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.

Title: Fast Implementation of the Iterative Proportional Fitting
Description: A fast (C) implementation of the iterative proportional fitting
procedure. Based on corresponding code from the networkTomography package.
Packaged: 2016-02-14 21:30:33 UTC; awblocker

Title: Fast implementation of the iterative proportional fitting
Packaged: 2013-07-09 04:15:35 UTC; awblocker

Title: List Balancing for Reweighting and Population Synthesis
"Kyle", "Ward", email = "kyleward084@gmail.com",
person("Greg", "Macfarlane", email = "gregmacfarlane@byu.edu", role = c("ctb")))
Description: Performs iterative proportional updating given a seed table and
an arbitrary number of marginal distributions. This is commonly used in
population synthesis, survey raking, matrix rebalancing, and other
applications. For example, a household survey may be weighted to match the
known distribution of households by size from the census. An origin/
destination trip matrix might be balanced to match traffic counts.
The approach used by this package is based on a paper from
Arizona State University (Ye, Xin, et. al. (2009)
Some enhancements have been made to their work including primary and
secondary target balance/importance, general marginal agreement, and weight
Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.2.1), magrittr (>= 1.5), tidyr