Basic Functions for Pre-Processing Microarrays
Provides classes to pre-process microarray gene
expression data as part of the OOMPA collection of packages
described at <http://oompa.r-forge.r-project.org/>.
R (>= 3.0), methods, graphics, stats, oompaBase (>= 3.0)

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

Rasmus E. Benestad <rasmus.benestad@physics.org> & D. Nychka
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
with amounts greater than 1mm/day.

Accurate Floating Point Sums and Products
c(person("Matthew","Fidler",
role = c("aut","cre","cph"), email = "matthew.fidler@gmail.com"),
person("Raymond","Hettinger",role=c("cph","aut")),
person("Jonathan", "Shewchuk",role=c("cph","aut")),
person("Julian", "Taylor",role=c("cph","aut")),
person("Nathaniel", "Smith",role=c("cph","aut")),
person("NumPy Team", role="cph"),
person("Python Team", role="cph")
)
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
(1965) sum <doi:10.1145/363707.363723>, Neumaier (1974) sum
<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
'Python' ; Shewchuk (1997)
<http://www.cs.berkeley.edu/~jrs/papers/robustr.pdf>).  In addition,
products are changed to long double precision for accuracy, or
changed into a log-sum for accuracy.
Matthew Fidler [aut, cre, cph],
Raymond Hettinger [cph, aut],
Jonathan Shewchuk [cph, aut],
Julian Taylor [cph, aut],
Nathaniel Smith [cph, aut],
NumPy Team [cph],
Python Team [cph]