Free newsletters samples software for linux

Oxylus Newsletters System is a complex newsletter system. It allows you to manage multiple groups.
You can import and export users, and public subscription is allowed. Oxylus Newsletters System project allows linux users to schedule newsletters for a specific date using a crontab job.
For each sent newsletter, you can see its history and whether it was successfully sent.
Enhancements:
- This version is fully compatible with PHP5.
- The mails are sent just once to all the users.
- The SQL error which occurred when new groups are created was fixed.
- Various framework bugfixes were made.

C2::Newsletter is a simple tool for sending newsletters to your visitors. It allows you to create multiple newsletters, so your visitors can select which topics they want to receive.
There is an admin interface to add/edit newsletters and recipients, and send in plain text or MIME-encoded. It is easy to use, but currently only in German (English will be available soon).
Enhancements:
new Features
- Verification email is now configurable (plain text only)
- Supports now the admin as recipient for verification emails
fixed Bugs:
- Admin interface allows to list the recipients of a specified newsletter
- Config page => cancel button now works
- deleting a newsletter deletes also items in c2_mail_newsletter_selections
- holds email when adding a new recipient record
- verification of new recipients now works
performance:
- function GetConfig()

CPPSERV is a web application server that provides Servlet-like API and JSP-like functionality to C++ programmers. CPPSERV consists of stand-alone daemon, listening on TCP socket for requests from web server, and web server module. Currently apache-2.0.x and ligttpd are supported.
CSP (C++ Server pages) are currently implemented as a precompiler that converts CSP source to C++ source of a servlet, which, in turn needs to be compiled into CPPSERV servlet. In other words - no run-time compilation/updates. This greatly simplifies the system, avoids long "first-time access" delay, and avoids dependancy on compiler and header files on the server.
Enhancements:
- This version features a completely non-interactive test suite for CPPSERV, an update to the latest SPTK API, and a few minor bugfixes.

Algorithm::Numerical::Sample is a Perl module that can draw samples from a set.

SYNOPSIS

use Algorithm::Numerical::Sample qw /sample/;

@sample = sample (-set => [1 .. 10000],
-sample_size => 100);

$sampler = Algorithm::Numerical::Sample::Stream -> new;
while () {$sampler -> data ($_)}
$random_line = $sampler -> extract;

This package gives two methods to draw fair, random samples from a set. There is a procedural interface for the case the entire set is known, and an object oriented interface when the a set with unknown size has to be processed.
A: sample (set => ARRAYREF [,sample_size => EXPR])

The sample function takes a set and a sample size as arguments. If the sample size is omitted, a sample of 1 is taken. The keywords set and sample_size may be preceeded with an optional -. The function returns the sample list, or a reference to the sample list, depending on the context.

B: Algorithm::Numerical::Sample::Stream

The class Algorithm::Numerical::Sample::Stream has the following methods:

new

This function returns an object of the Algorithm::Numerical::Sample::Stream class. It will take an optional argument of the form sample_size => EXPR, where EXPR evaluates to the sample size to be taken. If this argument is missing, a sample of size 1 will be taken. The keyword sample_size may be preceeded by an optional dash.

data (LIST)

The method data takes a list of parameters which are elements of the set we are sampling. Any number of arguments can be given.

extract

This method will extract the sample from the object, and reset it to a fresh state, such that a sample of the same size but from a different set, can be taken. extract will return a list in list context, or the first element of the sample in scalar context.

sloop.splitter is a realtime sound effect software.

It samples sounds from any source and dissects them. It searches for waveforms and repeats them from one repetition to thousands. So, it generates synthetic sounds from natural ones.

The waveforms are never deformed, only repeated, so always natural waveforms are played, but in a more or less synthetic way.

The number of repetitions constantly changes from one (the identical sound) to thousands and back. It tries to show the transition between the synthetic and the natural.

Apache Synapse is a mediation framework for Web Services. Synapse allows messages flowing through, into, or out of an organization to be mediated. Synapse, incidentally, is pronounced "sine-apse", and not "sin-apse".
Main features:
- A streamlined configuration model and a new XML syntax
- Proxy Services (service mediation) and Message mediation
- Concept of Endpoints
- Integration of a Registry and dynamic refresh of resources as well as rules used to mediate
- Support for http and JMS transports
- Support for WS-Reliable Messaging and WS-Security through WS-Policies
- Script mediator supporting all BSF scripting languages
- Support for error handling and recovery
- Support for WS-RM sequences
- Many built in mediators
- Maven 2 based build process
- Many samples and a built-in Axis2 server to try out and experiment with samples (Samples includes WS-Security, JMS POX/Text messages, Script mediation and many more samples which can be run out of the box)
- Enhanced documentation
Enhancements:
- Significant improvements including non-blocking HTTP and HTTPS transports, better samples, and better documentation.

Web Mailing List project designed to be an easy to use mailing list application.
Using a mailing list application can greatly enhance communication with your site visitors. Web mailing list makes it easy for users to signup for your mailing lists.
Creating and sending out email newsletters is a snap. If you your users wish to unsubscribe, they simply visit your unsubscribe page and enter the email address where they received the newslettter.
Enhancements:
- This release updates the database creation portion of the system setup.

Statistics::TTest is a Perl module to perform T-test on 2 independent samples.

Statistics::TTest::Sufficient - Perl module to perfrom T-Test on 2 indepdent samples using sufficient statistics

SYNOPSIS

#example for Statistics::TTest
use Statistics::PointEstimation;
use Statistics::TTest;
my @r1=();
my @r2=();
my $rand;

for($i=1;$iset_significance(90);
$ttest->load_data(@r1,@r2);
$ttest->output_t_test();
$ttest->set_significance(99);
$ttest->print_t_test(); #list out t-test related data

#the following thes same as calling output_t_test() (you can check if $ttest->{valid}==1 to check if the data is valid.)
my $s1=$ttest->{s1}; #sample 1 a Statistics::PointEstimation object
my $s2=$ttest->{s2}; #sample 2 a Statistics::PointEstimation object
print "*****************************************************nn";
$s1->output_confidence_interval(1);
print "*****************************************************nn";
$s2->output_confidence_interval(2);
print "*****************************************************nn";

print "Comparison of these 2 independent samples.n";
print "t F-statistic=",$ttest->f_statistic()," , cutoff F-statistic=",$ttest->f_cutoff(),
" with alpha level=",$ttest->alpha*2," and df =(",$ttest->df1,",",$ttest->df2,")n";
if($ttest->{equal_variance})
{ print "tequal variance assumption is accepted(not rejected) since F-statistic < cutoff F-statisticn";}
else
{ print "tequal variance assumption is rejected since F-statistic > cutoff F-statisticn";}

print "tdegree of freedom=",$ttest->df," , t-statistic=T=",$ttest->t_statistic," Prob >|T|=",$ttest->{t_prob},"n";
print "tthe null hypothesis (the 2 samples have the same mean) is ",$ttest->null_hypothesis(),
" since the alpha level is ",$ttest->alpha()*2,"n";
print "tdifference of the mean=",$ttest->mean_difference(),", standard error=",$ttest->standard_error(),"n";
print "t the estimate of the difference of the mean is ", $ttest->mean_difference()," +/- ",$ttest->delta(),"nt",
" or (",$ttest->lower_clm()," to ",$ttest->upper_clm," ) with ",$ttest->significance," % of confidencen";

#example for Statistics::TTest::Sufficient
use Statistics::PointEstimation;
use Statistics::TTest;

my %sample1=(
count =>30,
mean =>3.98,
variance =>2.63
);

my %sample2=(
count=>30,
mean=>3.67,
variance=>1.12
);


my $ttest = new Statistics::TTest::Sufficient;
$ttest->set_significance(90);
$ttest->load_data(%sample1,%sample2);
$ttest->output_t_test();
#$ttest->s1->print_confidence_interval();
$ttest->set_significance(99);
$ttest->output_t_test();
#$ttest->s1->print_confidence_interval();

Statistics::TTest

This is the Statistical T-Test module to compare 2 independent samples. It takes 2 array of point measures, compute the confidence intervals using the PointEstimation module (which is also included in this package) and use the T-statistic to test the null hypothesis. If the null hypothesis is rejected, the difference will be given as the lower_clm and upper_clm of the TTest object.

Statistics::TTest::Sufficient

This module is a subclass of Statistics::TTest. Instead of taking the real data points as the input, it will compute the confidence intervals based on the sufficient statistics and the sample size inputted. To use this module, you need to pass the sample size, the sample mean , and the sample variance into the load_data() function. The output will be exactly the same as the Statistics::TTest Module.