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The ellipsis is a powerful tool for extending functions. Unfortunately
this power comes at a cost: misspelled arguments will be silently
ignored. The ellipsis package provides a collection of functions to
catch problems and alert the user.
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Functions introduced or changed since R v3.0.0 are re-implemented in
this package. The backports are conditionally exported in order to let
R resolve the function name to either the implemented backport, or the
respective base version, if available. Package developers can make use
of new functions or arguments by selectively importing specific
backports to support older installations.
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Two nonparametric methods for multiple regression transform selection
are provided. The first, Alternative Conditional Expectations (ACE),
is an algorithm to find the fixed point of maximal correlation, i.e.
it finds a set of transformed response variables that maximizes R^2
using smoothing functions [see Breiman, L., and J.H. Friedman. 1985.
"Estimating Optimal Transformations for Multiple Regression and
Correlation". Journal of the American Statistical Association.
80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is
the Additivity Variance Stabilization (AVAS) method which works better
than ACE when correlation is low [see Tibshirani, R.. 1986.
"Estimating Transformations for Regression via Additivity and Variance
Stabilization". Journal of the American Statistical Association.
83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction
to these two methods is in chapter 16 of Frank Harrel's "Regression
Modeling Strategies" in the Springer Series in Statistics.
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Various statistical, graphics, and data-management functions used by
the Rcmdr package in the R Commander GUI for R.
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Various utilities are provided that might be used in spatial
statistics and elsewhere. It delivers a method for solving linear
equations that checks the sparsity of the matrix before any algorithm
is used. Furthermore, it includes the Struve functions.
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Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side (see
<DOI:10.18637/jss.v034.i01>).
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This is the last version that supports Python 2.7.
Apparently, some packages still require Scipy with Python 2.7.
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uncertainties allows calculations such as (2 +/- 0.1)*2 = 4 +/- 0.2 to be
performed transparently. Much more complex mathematical expressions involving
numbers with uncertainties can also be evaluated directly.
The uncertainties package takes the pain and complexity out of uncertainty
calculations.
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An R interface to the NetCDF file format designed by Unidata for
efficient storage of array-oriented scientific data and descriptions.
The R interface is closely based on the C API of the NetCDF library,
and it includes calendar conversions from the Unidata UDUNITS library.
The current implementation supports all operations on NetCDF datasets
in classic and 64-bit offset file formats, and NetCDF4-classic format
is supported for reading and modification of existing files.
From Kai-Uwe Eckhardt, updated as the previous distfile wasn't available.
PR pkg/51607
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Use math/gnumeric112 instead.
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Changes from 3.4.3 to 3.4.4
Improvements
Environment variable to control the use of embedded libraries.
Include citation in repository. gh-690.
Bugs fixed
Fixed import error with numexpr 2.6.5.dev0 gh-685.
Fixed linter warnings.
Fixed for re.split() is version detection. gh-687.
Fixed test failures with Python 2.7 and NumPy 1.14.3 gh-688 & gh-689.
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Added math/p5-Math-Systems version 0.01 [mef 2018-06-20]
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Kiwi is an efficient C++ implementation of the Cassowary constraint solving
algorithm. Kiwi is an implementation of the algorithm based on the seminal
Cassowary paper. It is not a refactoring of the original C++ solver. Kiwi has
been designed from the ground up to be lightweight and fast. Kiwi ranges from
10x to 500x faster than the original Cassowary solver will typical use cases
gaining a 40x improvement. Memory savings are consistently > 5x
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ASTEVAL is a safe(ish) evaluator of Python expressions and statements, using
Python's ast module. The idea is to provide a simple, safe, and robust
miniature mathematical language that can handle user-input. The emphasis here
is on mathematical expressions, and so many functions from numpy are imported
and used if available.
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This package was obsoleted by math/py-tensorboard.
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Perl5 module that implements a simple bloom filter in C/XS
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