BPHO
1.3-0Bayesian Prediction with High-order Interactions
Overview
This software can be used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discreate covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. The models are trained with slice sampling method, a variant of Markov chain Monte Carlo. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step.
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Code & Tests
People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 3.0.0 released · 2013-04-03
- 1.3-02012-10-29 · diff ↗
- 1.3-0archived2012-10-29 · diff ↗
- RR 2.15.0 released · 2012-03-30
- RR 2.14.0 released · 2011-10-31
- RR 2.13.0 released · 2011-04-13
- RR 2.12.0 released · 2010-10-15
- RR 2.11.0 released · 2010-04-22
- RR 2.10.0 released · 2009-10-26
- RR 2.9.0 released · 2009-04-17
- RR 2.8.0 released · 2008-10-20
- RR 2.7.0 released · 2008-04-22
- 1.2-52008-04-07 · diff ↗
- 1.2-42008-04-02 · diff ↗
- 1.2-32008-02-22 · diff ↗
- 1.2-22008-02-21 · diff ↗
Show 2 earlier events
- 1.2-12008-02-20
- RR 2.6.0 released · 2007-10-03
Package metadata
- Total releases
- 6
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.5.1
- Download size
- not tracked yet
- Installed size
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- With dependencies
- not tracked yet
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Run in R for the authors' preferred citation:
citation("BPHO")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
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