Software Map
Project Tree
Now limiting view to projects in the following categories:
Intended Audience :: Other Audience [Remove This Filter]
Intended Audience :: End Users/Desktop [Remove This Filter]
Topic :: Robust Statistics [Remove This Filter]
Programming Language :: R [Remove This Filter]
3 projects in result set.
0. Structural equation modeling - Several SEM related packages are developed for conducting structural equation modeling.
|
- Development Status : 5 - Production/Stable [Filter]
- Intended Audience : Developers [Filter]
- Intended Audience : End Users/Desktop (Now Filtering)
- Intended Audience : Other Audience (Now Filtering)
- License : OSI Approved : GNU General Public License (GPL) [Filter]
- Natural Language : English [Filter]
- Operating System : MacOS [Filter]
- Operating System : Microsoft : Windows [Filter]
- Operating System : POSIX : Linux [Filter]
- Programming Language : R (Now Filtering)
- Topic : Multivariate Statistics : Latent Variable Approaches [Filter]
- Topic : Robust Statistics (Now Filtering)
|
Activity Percentile: 0.00
Registered: 2011-09-26 14:13 |
1. Dependency modeling toolbox - Model-based tools for the discovery and analysis of statistical dependencies between data sources.
|
- Development Status : 5 - Production/Stable [Filter]
- Environment : Console (Text Based) [Filter]
- Environment : Other Environment [Filter]
- Intended Audience : Developers [Filter]
- Intended Audience : End Users/Desktop (Now Filtering)
- Intended Audience : Other Audience (Now Filtering)
- License : OSI Approved : BSD License [Filter]
- Natural Language : English [Filter]
- Operating System : OS Independent [Filter]
- Programming Language : R (Now Filtering)
- Topic : Machine Learning : Bayesian Methods [Filter]
- Topic : Multivariate Statistics : Latent Variable Approaches [Filter]
- Topic : Robust Statistics (Now Filtering)
|
Activity Percentile: 0.00
Registered: 2010-04-08 11:22 |
2. Generalized iLUCK-models - Interval/imprecise probability models allowing non-stochastic uncertainty in Bayesian analysis by defining sets of conjugate priors such that the set of posteriors, obtained by updating each prior in the set by Bayes' rule, is still easy to handle.
|
- Development Status : 2 - Pre-Alpha [Filter]
- Intended Audience : Developers [Filter]
- Intended Audience : End Users/Desktop (Now Filtering)
- Intended Audience : Other Audience (Now Filtering)
- License : OSI Approved : GNU General Public License (GPL) [Filter]
- Programming Language : R (Now Filtering)
- Topic : Bayesian Statistics [Filter]
- Topic : Other/Nonlisted Topic [Filter]
- Topic : Robust Statistics (Now Filtering)
|
Activity Percentile: 0.00
Registered: 2010-03-06 00:28 |
