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4 projects in result set.
0. Point Process Statistics - This package implements statistical methods for one-dimensional marked
point process models.
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- Development Status : 4 - Beta (Now Filtering)
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- License : OSI Approved : GNU General Public License (GPL) (Now Filtering)
- Natural Language : English [Filter]
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- Topic : Machine Learning : Model Selection and Validation [Filter]
- Topic : Machine Learning : Regularized and Shrinkage Methods (Now Filtering)
- Topic : Spatial Data & Statistics : Point Pattern Analysis [Filter]
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Activity Percentile: 0.00
Registered: 2010-08-05 07:40 |
1. Sparse LDA - This package implements elasticnet-like sparseness in linear and mixture discriminant analysis as described in "Sparse Discriminant Analysis" by Line Clemmensen, Trevor Hastie and Bjarne Ersb
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- Development Status : 4 - Beta (Now Filtering)
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- Topic : Machine Learning : Regularized and Shrinkage Methods (Now Filtering)
- Topic : Multivariate Statistics : Supervised Classification and Discriminant Analysis [Filter]
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Registered: 2009-01-22 16:44 |
2. Clustering using convex fusion penalties - An R/C++ implementation of the clusterpath algorithm described in Hocking et al. 2011, for robust convex clustering using sparsity-inducing fusion penalties.
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- Development Status : 4 - Beta (Now Filtering)
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- Programming Language : Python [Filter]
- Programming Language : R [Filter]
- Topic : Cluster Analysis : Hierarchical Clustering [Filter]
- Topic : Machine Learning : Regularized and Shrinkage Methods (Now Filtering)
- Topic : Optimization [Filter]
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Registered: 2011-05-09 12:56 |
3. CoxFlexBoost - CoxFlexBoost:
likelihood-based boosting approach to fit structured Cox-type survival models with linear, smooth and (linear/smooth) time-varying effects.
By applying a component-wise boosting approach variable selection and model choice are possible.
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- License : OSI Approved : GNU General Public License (GPL) (Now Filtering)
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- Programming Language : R [Filter]
- Topic : Machine Learning : Boosting [Filter]
- Topic : Machine Learning : Model Selection and Validation [Filter]
- Topic : Machine Learning : Regularized and Shrinkage Methods (Now Filtering)
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Registered: 2008-10-30 14:30 |
