./math/p5-Statistics-TTest, Perl module of confidence interval computation using T-Tests

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Branch: CURRENT, Version: 1.1.0nb2, Package name: p5-Statistics-TTest-1.1.0nb2, Maintainer: pkgsrc-users

Perl module for computing confidence intervals in parameter estimation
with Student's T distribution.


Required to run:
[lang/perl5] [math/p5-Statistics-Descriptive] [math/p5-Statistics-Distributions]

Master sites: (Expand)

SHA1: fba1dcd047638d6706c5db2048a01aff49ab1cb4
RMD160: 68544866a5c7fece5a53e8a4ef24758d4be72f24
Filesize: 5.872 KB

Version history: (Expand)


CVS history: (Expand)


   2016-06-08 21:25:20 by Thomas Klausner | Files touched by this commit (2236) | Package updated
Log message:
Bump PKGREVISION for perl-5.24.
   2015-11-04 00:33:46 by Alistair G. Crooks | Files touched by this commit (262)
Log message:
Add SHA512 digests for distfiles for math category

Problems found locating distfiles:
	Package dfftpack: missing distfile dfftpack-20001209.tar.gz
	Package eispack: missing distfile eispack-20001130.tar.gz
	Package fftpack: missing distfile fftpack-20001130.tar.gz
	Package linpack: missing distfile linpack-20010510.tar.gz
	Package minpack: missing distfile minpack-20001130.tar.gz
	Package odepack: missing distfile odepack-20001130.tar.gz
	Package py-networkx: missing distfile networkx-1.10.tar.gz
	Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz
	Package quadpack: missing distfile quadpack-20001130.tar.gz

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.
   2015-06-12 12:52:19 by Thomas Klausner | Files touched by this commit (3152)
Log message:
Recursive PKGREVISION bump for all packages mentioning 'perl',
having a PKGNAME of p5-*, or depending such a package,
for perl-5.22.0.
   2014-08-13 22:35:24 by Thomas Klausner | Files touched by this commit (3)
Log message:
Import p5-Statistics-TTest-1.1.0 as math/p5-Statistics-TTest.

Perl module for computing confidence intervals in parameter estimation
with Student's T distribution.