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8 projects in result set.
0. Finite Mixure of AFT and FMR models - FMRS package provides estimation and variable selection in Finite Mixture of Accelerated Failure Time Regression (FMAFTR) and Finite Mixture of Regression (FMR) models with a large number of covariates and/or right censoring and heterogeneous structure. |
- Development Status : 5 - Production/Stable [Filter]
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- License : OSI Approved : GNU General Public License (GPL) [Filter]
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- Programming Language : R [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Regression Models [Filter]
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Registered: 2016-03-07 02:31 |
1. 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. |
- Development Status : 4 - Beta [Filter]
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- Programming Language : R [Filter]
- Topic : Machine Learning : Boosting [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Machine Learning : Regularized and Shrinkage Methods [Filter]
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Registered: 2008-10-30 14:30 |
2. addendum - Utility functions that should have been in R: easy glmnet, pretty graphing and swift debugging and profiling. |
- Development Status : 5 - Production/Stable [Filter]
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- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Multivariate Statistics : Missing Data [Filter]
- Topic : Regression Models [Filter]
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Registered: 2011-06-17 13:35 |
3. Classification and Regression Training - The caret packages contain functions for tuning predictive models, pre-processing, variable importance and other tools related to machine learning and pattern recognition. Parallel processing versions of the main package are also included. |
- Development Status : 5 - Production/Stable [Filter]
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- Operating System : OS Independent [Filter]
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- Programming Language : R [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
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Registered: 2008-06-05 19:31 |
4. Tools for Uplift Modeling - The R Package tools4uplift integrates some tools for exploring and modeling uplift. The content can be separated into the following steps of statistical modeling: quantization, visualization, model selection and model validation. |
- Development Status : 4 - Beta [Filter]
- Environment : Console (Text Based) [Filter]
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- Topic : Graphics : Plotting [Filter]
- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
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Registered: 2018-08-31 21:23 |
5. The Degrees of Freedom of PLS - The package provides Degrees of Freedom of Partial Least Squares. Model selection based on information criteria can be performed.
Krämer & Braun (2007) Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection, ICML-07, 441 - 448 |
- Development Status : 1 - Planning [Filter]
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- Topic : Machine Learning : Model Selection and Validation (Now Filtering)
- Topic : Multivariate Statistics : Projection Methods [Filter]
- Topic : Regression Models [Filter]
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Registered: 2009-10-08 14:16 |
6. Point Process Statistics - This package implements statistical methods for one-dimensional marked
point process models. |
- Development Status : 4 - Beta [Filter]
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- Programming Language : Fortran [Filter]
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- Topic : Machine Learning : Regularized and Shrinkage Methods [Filter]
- Topic : Spatial Data & Statistics : Point Pattern Analysis [Filter]
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Registered: 2010-08-05 07:40 |
7. QSAR Data Sets - Molecular descriptors and outcomes for several public domain data sets |
- Development Status : 4 - Beta [Filter]
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- Topic : Chemoinformatics [Filter]
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Registered: 2010-09-19 03:40 |