./math/R-prob, Elementary probability on finite sample spaces

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Branch: CURRENT, Version: 1.0.0, Package name: R-prob-1.0.0, Maintainer: pkgsrc-users

A framework for performing elementary probability calculations on
finite sample spaces, which may be represented by data frames or
lists. Functionality includes setting up sample spaces, counting
tools, defining probability spaces, performing set algebra,
calculating probability and conditional probability, tools for
simulation and checking the law of large numbers, adding random
variables, and finding marginal distributions. Characteristic
functions for all base R distributions are included.


Required to run:
[math/R] [math/R-combinat] [finance/R-fAsianOptions]

Required to build:
[pkgtools/cwrappers]

Master sites: (Expand)


Version history: (Expand)


CVS history: (Expand)


   2018-07-28 16:53:46 by Brook Milligan | Files touched by this commit (2)
Log message:
Add the CRAN archives to R package MASTER_SITES.

Distfiles for older R packges are moved on CRAN from contrib/ to
contrib/Archive/${R_PKGNAME}/.  As a result, they may be inaccessible to
'make fetch'.  Add the additional directory to MASTER_SITES in
math/R/Makefile.extension so that older distfiles will be retrieved
directly from CRAN.  In addition, remove the definition of MASTER_SITES
from math/R-prob/Makefile, which is the only package directly referring to
the distfile archive.  At this point all R packages should rely on
math/R/Makefile.extension to define MASTER_SITES; they should not be
defining MASTER_SITES directly.
   2018-03-02 20:04:14 by Min Sik Kim | Files touched by this commit (3)
Log message:
math/R-prob: Import version 1.0.0

A framework for performing elementary probability calculations on
finite sample spaces, which may be represented by data frames or
lists.  Functionality includes setting up sample spaces, counting
tools, defining probability spaces, performing set algebra,
calculating probability and conditional probability, tools for
simulation and checking the law of large numbers, adding random
variables, and finding marginal distributions.  Characteristic
functions for all base R distributions are included.