Added math/py-asdf version 2.7.1
Added devel/py-extension-helpers version 0.1
Added math/py-astropy version 4.2
Added math/py-jplephem version 2.15
Added math/py-pyerfa version 1.7.1.1
Added math/py-pyvo version 1.1
Added math/py-astroquery version 0.3.9
Added math/py-astroML version 1.0
Added math/py-gwcs version 0.15.0
Added math/py-photutils version 1.0.1
Added math/py-astroplan version 0.7
The distfile contains invalid "." and ".." entries.
Both bsdtar and gtar refuse to unpack this.
The package is not used by any other packages in pkgsrc and upstream is
dead (distfile is from 2004).
Support for measurement units in R vectors, matrices and arrays:
automatic propagation, conversion, derivation and simplification of
units; raising errors in case of unit incompatibility. Compatible with
the POSIXct, Date and difftime classes. Uses the UNIDATA udunits
library and unit database for unit compatibility checking and
conversion.
A system for writing hierarchical statistical models largely
compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate
models and do basic R-style math, and compiling both models and
nimbleFunctions via custom- generated C++. 'NIMBLE' includes default
methods for MCMC, particle filtering, Monte Carlo Expectation
Maximization, and some other tools. The nimbleFunction system makes it
easy to do things like implement new MCMC samplers from R, customize
the assignment of samplers to different parts of a model from R, and
compile the new samplers automatically via C++ alongside the samplers
'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by
making it extensible: New distributions and functions can be added,
including as calls to external compiled code. Although most people
think of MCMC as the main goal of the 'BUGS'/'JAGS' language for
writing models, one can use 'NIMBLE' for writing arbitrary other kinds
of model-generic algorithms as well. A full User Manual is available
at <https://r-nimble.org>.
LearnBayes contains a collection of functions helpful in learning the
basic tenets of Bayesian statistical inference. It contains functions
for summarizing basic one and two parameter posterior distributions
and predictive distributions. It contains MCMC algorithms for
summarizing posterior distributions defined by the user. It also
contains functions for regression models, hierarchical models,
Bayesian tests, and illustrations of Gibbs sampling.
A general purpose toolbox for personality, psychometric theory and
experimental psychology. Functions are primarily for multivariate
analysis and scale construction using factor analysis, principal
component analysis, cluster analysis and reliability analysis,
although others provide basic descriptive statistics. Item Response
Theory is done using factor analysis of tetrachoric and polychoric
correlations. Functions for analyzing data at multiple levels include
within and between group statistics, including correlations and factor
analysis. Functions for simulating and testing particular item and
test structures are included. Several functions serve as a useful
front end for structural equation modeling. Graphical displays of
path diagrams, factor analysis and structural equation models are
created using basic graphics. Some of the functions are written to
support a book on psychometric theory as well as publications in
personality research. For more information, see the
personality-project.org/r web page.
Functions are provided for computing the density and the distribution
function of multivariate normal and "t" random variables, and for
generating random vectors sampled from these distributions.
Probabilities are computed via non-Monte Carlo methods; different
routines are used in the case d=1, d=2, d>2, if d denotes the number
of dimensions.
The tensor product of two arrays is notionally an outer product of the
arrays collapsed in specific extents by summing along the appropriate
diagonals.
Gappa is a tool intended to help verifying and formally proving
properties on numerical programs dealing with floating-point or
fixed-point arithmetic.
Sollya is a tool environment and a library for safe floating-point
code development, particularly targeted at automated implementation
of math libraries like libm.
Derived from wip/sollya.
