1. Marginal Product Mixture Model- Implementation of the marginal product mixture model, which clusters event data arising in one, two, or three-mode networks. Can be useful for exploratory data analysis and predictions about missing or future events.
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.
5. Genome-wide analysis using MOSS- Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. Includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
6. 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.
7. dcr - Data Cloning in R- Data cloning (DC) uses Bayesian MCMC to make maximum likelihood inference of complex hierarchical models. The bundle includes basic infrastructure for DC with parallel computing support, and more specialized packages for ecology.
Development Status : 5 - Production/Stable [Filter]
Topic : Spatial Data & Statistics : Geostatistics [Filter]
Activity Percentile: 0.00
Registered: 2011-08-18 03:20
10. Moment and Inverse Moment Bayes factors- Performs model selection based on non-local priors, including MOM, eMOM and iMOM priors. Routines are provided to compute Bayes factors, marginal densities and to perform variable selection in regression setups.