Download JAR files tagged by australia with all dependencies

A collection of Java artefacts for Consumer Data Standards Australia

jclouds components to access Fujitsu Global Cloud Platform in Australia

jclouds components to access Fujitsu Global Cloud Platform in Australia

jclouds components to access Fujitsu Global Cloud Platform in Australia

Provides a pure Java based linear algebra library with interfaces similar to the Armadillo C++ algebra library (Armadillo) by Conrad Sanderson et al., NICTA, Australia.

This API determines the holidays for a given year, country/name and eventually state/region. The holiday data is stored in XML files (one for each country) and will be read from the classpath. You can provide your own holiday calendar XML file or use any of the provided ones. Currently, there are over 80 countries supported like the following: * United States * Germany * France * India * Australia * and so on Besides, those there will be more special calendars like currently existing NYSE calendar (New York Stock Exchange).

This API determines the holidays for a given year, country/name and eventually state/region. The holiday data is stored in XML files (one for each country) and will be read from the classpath. You can provide your own holiday calendar XML file or use any of the provided ones. Currently there are 63 countries supported like the following: United States, most european countries, Russia, India, Australia. Besides those there will be more special calendars like currently existing NYSE calendar (New York Stock Exchange).

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20) For more information see: Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand. Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002.