Title: Pre-Knitting Processing and Post HTML-Rendering Processing
Description: Dynamize headers or R code within 'Rmd' files to prevent proliferation of 'Rmd' files for similar reports. Add in external HTML document within 'rmarkdown' rendered HTML doc.
Imports: utils, tools, XML (>= 3.98-1.4), knitr (>= 1.13), rmarkdown
SystemRequirements: pandoc (>= 1.12.3) -

Title: Basic Functions for Pre-Processing Microarrays
Description: Provides classes to pre-process microarray gene
expression data as part of the OOMPA collection of packages
Depends: R (>= 3.0), methods, graphics, stats, oompaBase (>= 3.0)

Packaged: 2019-05-06 12:38:02 UTC; Kevin Coombes

Title: Various Preprocessing Transformations of Numeric Data Matrices
Description: Preprocess numeric data matrices into desired transformed representations.
Standardization, Unitization, Cubitization and adaptive intervals are offered.

Author: Rasmus E. Benestad <rasmus.benestad@physics.org> & D. Nychka
Maintainer: Rasmus E. Benestad <rasmus.benestad@physics.org>
Description: The package contains data for the qqplotter package. These
data contain 14 percentiles p in [0.50, 0.55, ... 0.95] and
[0.96,0.97, ... 0.99] for the 24hr precipitation for all days

Title: Accurate Floating Point Sums and Products
role = c("aut","cre","cph"), email = "matthew.fidler@gmail.com"),
Description: Most of the time floating point arithmetic does
approximately the right thing.  When adding sums or having products
of numbers that greatly differ in magnitude, the floating point
arithmetic may be incorrect.  This package implements the Kahan
<doi:10.1002/zamm.19740540106>, pairwise-sum (adapted from 'NumPy',
See Castaldo (2008) <doi:10.1137/070679946> for a discussion of
accuracy), and arbitrary precision sum (adapted from the fsum in
products are changed to long double precision for accuracy, or

Title: Computational Prediction of Proteins Encoded by Circadian Genes
Author: Prabina Kumar Meher <meherprabin@yahoo.com>
Description: A computational model for predicting proteins encoded by circadian genes. The support vector machine has been employed with Laplace kernel for prediction of circadian proteins, where compositional, transitional and physico-chemical features were utilized as numeric features. User can predict for the test dataset using the proposed computational model. Besides, the user can also build their own training model using their training dataset, followed by prediction for the test set.