X-CRAN-Comment: Orphaned on 2018-06-19: all updates have been by the
CRAN team for some years.  Maintainership formally assumed

Description: XML2R is a collection of convenient functions for coercing XML
into multiple matrices or data frame(s).
Packaged: 2013-10-19 21:23:24 UTC; cpsievert

Packaged: 2014-01-04 19:46:06 UTC; cpsievert

Description: XML2R is a framework that reduces the effort required to transform
XML content into number of tables while preserving parent to child
Packaged: 2014-01-16 06:27:04 UTC; cpsievert

Packaged: 2014-01-23 02:10:17 UTC; cpsievert

Packaged: 2014-03-03 21:52:06 UTC; cpsievert

Packaged: 2014-03-09 21:06:15 UTC; cpsievert

Description: A package that reads XML schema into an R representation
and can perform some operations on the resulting information to
generate class definitions and code to read documents using
this schema.  This is code that was originally in the SSOAP
package for reading WSDL files that describe Web Services and
Collate: ClassDefs.S processSchemaTypes.R SOAPTypes.S resolve.R
genCode.R parseSchema.R createConverters.R schema.R array.S
basicTypes.R utils.R selfRefs.R enums.R restriction.R
fixTypeNames.R serialize.R toXML.R dependencies.R validity.R

Title: Markov Random Fields for High-Throughput Genetics Data
Author: Ying-Wooi Wan, Genevera I. Allen, Yulia Baker, Eunho Yang, Pradeep Ravikumar, Zhandong Liu
Imports: igraph, glmnet, MASS, snowfall, parallel, Matrix
Description: Fit Markov Networks to a wide range of high-throughput genomics data.

Title: Exact Goodness-Of-Fit Test For Multinomial Data With Fixed
Description: Tests whether a set of counts fit a given expected ratio. For
example, a genetic cross might be expected to produce four types in the
relative frequencies of 9:3:3:1. To see whether a set of observed counts
fits this expectation, one can examine all possible outcomes (multi) or a
random sample of them (xmonte) and find the probability of an observation
deviating from the expectation by at least as much as the observed. As a
measure of deviation from the expected, one can use the log-likelihood
ratio, the multinomial probability, or the classic chi-square statistic. A
histogram of the test statistic can also be plotted and compared with the