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16 projects in result set.
0. memisc
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Registered: 2013-02-08 10:55 |
1. spatial copula - This package has moved to: https://github.com/bengraeler/spcopula
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- Topic : Multivariate Statistics : Modelling Non-Gaussian Data [Filter]
- Topic : Multivariate Statistics : Multivariate Distributions : Copulae [Filter]
- Topic : Spatial Data & Statistics : Geostatistics [Filter]
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Registered: 2011-03-21 17:39 |
2. WMCapacity - The WMCapacity project implements hierarchical Bayesian models of working memory capacity, including Cowan's (2001) and Pashler's (1988) multinomial tree models. An SPSS-like GUI is provided for model building and MCMC setup.
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- Topic : Bayesian Statistics : R Interfaces [Filter]
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- Topic : Social Sciences : Analysis of Categorical and Count Data [Filter]
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Registered: 2009-09-25 14:45 |
3. Earth Mover Distance - EarthMovDist implements the Earth Movers distance in R.
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Registered: 2009-02-17 06:53 |
4. Profile Dirichlet Process Mixtures - This package facilitates profile inference for a class of product partition models (PPM). The Dirichlet process mixture is a specific case in this class. These methods search for the maximum posterior (MAP) estimate for the cluster partition in the PPM.
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Registered: 2009-04-24 15:56 |
5. partykit: Recursive Partytioning Toolkit - Infrastructure for representing, summarizing, and visualizing tree-structured models. It can be used for constructing or reading tree models from different sources (rpart, RWeka, PMML), sharing
functionality for print/plot/predict methods.
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- Topic : Machine Learning : Recursive Partitioning [Filter]
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Registered: 2008-09-30 11:39 |
6. Models for Data from Unmarked Animals - This is a stub. Development now continues at http://github.com/ianfiske/unmarked.
unmarked analyzes wildlife data arising from many popular wildlife sampling techniques using 2-stage hierarchical models.
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- Topic : Spatial Data & Statistics : Ecological Analysis [Filter]
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Registered: 2007-07-25 14:05 |
7. casper - casper infers alternative splicing from high-throughput sequencing data both for known variants and de novo discovery. We use a Bayesian model with few assumptions, and modern model selection ideas with improved theoretical and computational properties.
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Registered: 2011-10-11 08:51 |
8. waveslim: Wavelet methods for 1/2/3D - Basic wavelet routines for the processing/analysis/inference of time series, images and 3D arrays. The code provided here is based on Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); Kingsbury (1999); and Selesnick (200?).
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Registered: 2009-02-26 17:20 |
9. Hyper-g Priors for GAM selection - This R-package implements objective Bayesian model selection with penalised splines, using a so-called hyper-g prior for the parameters in the generalised additive model.
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Registered: 2011-08-11 09:12 |
10. TopKLists - The package includes tools for inference, aggregation, and visualization of multiple ranked lists. Consolidated information from two or more such lists comprising the same set of objects can be obtained. Incomplete rankings and partial lists are allowed.
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Registered: 2010-05-06 10:59 |
11. gsDesign: Group Sequential Design - Generates group sequential designs using alpha- and beta-spending functions or Wang-Tsiatis (e.g., O'Brien-Fleming and Pocock) boundary families. Many plotting capabilities as well as non-binding futility rules are available. Easily extended.
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Registered: 2008-08-31 13:07 |
12. splinesurv - A package containing utilities for nonparametric Bayesian analysis of clustered survival data. The baseline hazard function and frailty density are modeled using penalized B-splines. Regression and spline parameters are estimated via MCMC.
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Registered: 2008-01-08 16:50 |
13. Conditional Random Fields - This project provides a set of tools for conditional random fields tasks such as decoding/inference/sampling/training.
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Registered: 2010-12-28 07:13 |
14. Corbi - A collection of bioinformatics tools for general purposes such as network query, protein structure alignment, gene expression analysis.
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Registered: 2011-09-26 05:04 |
15. Markov decision processes (MDPs) in R - Create and optimize MDPs with discrete time steps and state space. Both normal MDPs and hierarchical MDPs can be considered.
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Registered: 2009-06-08 12:06 |
