https://github.com/brandonwillard/particlebayes
Bayesian Particle Learning models in R
https://github.com/brandonwillard/particlebayes
bayesian particle-filters real-time-processing state-space-models time-series
Last synced: 3 months ago
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Bayesian Particle Learning models in R
- Host: GitHub
- URL: https://github.com/brandonwillard/particlebayes
- Owner: brandonwillard
- Created: 2020-03-12T19:40:13.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-10-13T21:46:49.000Z (over 4 years ago)
- Last Synced: 2025-01-13T09:12:00.187Z (4 months ago)
- Topics: bayesian, particle-filters, real-time-processing, state-space-models, time-series
- Language: R
- Homepage:
- Size: 12.2 MB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
ParticleBayes
=====
ParticleBayes is an R package that contains a collection of
[particle filters](http://en.wikipedia.org/wiki/Particle_filter)
for a subset of hierarchical bayesian models, with a focus on sequentially
learning model parameters alongside state variables. The R code calls an
open-source Java library, [ParticleLearningModels][plm], to perform calculations,
so when/if a practitioner wants to implement a model in production,
the underlying, streaming-capable, API is available.
Most models are formulated as [dynamic linear models][dlm], or mixtures thereof,
although some support for hidden markov models exists.
The models also implement
* [Water-filling resampling][waterfilling]
* Parameter learning, via [Particle Learning][pl] where applicable
Currently, the implemented filters cover two response types:
* Multinomial/Categorical with a logistic link function
* 10-component [normal mixture approximation][fs1]
* [TBD][fs2]
* Multivariate Gaussian
and, naturally, their univariate equivalents.
Installation from R
==
ParticleBayes depends on the rJava R package, so make sure it's installed and
operational. This also means you'll need to have a functional JRE, so if you
don't you can get one from
[here](http://www.oracle.com/technetwork/java/javase/downloads/java-archive-downloads-javase6-419409.html#jre-6u45-oth-JPR),
install, and then run ```R CMD javareconf``` (with sudo if you're using linux)
at the command line. If you're using a Mac, you might need to run something
like ```R CMD javareconf JAVA_CPPFLAGS=-I/System/Library/Frameworks/JavaVM.framework/Headers```
instead. Pay attention for any errors in the output, and look around the internet
for more information.
While ParticleBayes is still in it's beta stages, installation in R requires
the [devtools][dt] package and something like the following:
```
library(devtools)
install_url("https://bitbucket.org/brandonwillard/particlebayes/get/HEAD.zip", subdir="pkg")
```
then, as usual, add ```library(ParticleBayes)``` to your R source to use.
NOTE: This will pick up the most recently pushed version of the code (i.e. the
jar file in ```pkg/inst/java```). Inclusion of this file in the repo is temporary,
that is, until this package gets submitted to CRAN.
Development Setup and Installation
==
ParticleBayes uses [Maven](http://maven.apache.org/), so you'll need to download that.
Once you have it,
```mvn clean package -Dmaven.test.skip=true```
should create the necessary jars in ```pkg/inst/java```, so that the code is callable from
R.
The java code is called, within R, through [rJava][rj]
Similar to the R installation, and from the project's root directory,
one possible development setup script could include
```r
library(devtools)
dev_mode(on=T)
system("mvn clean package -Dmaven.test.skip=true")
install_local("./pkg")
options(java.parameters=
c("-Xmx2g",
"-Xdebug",
"-Xrunjdwp:server=y,transport=dt_socket,address=4001,suspend=n"))
library(ParticleBayes)
...
dev_mode(on=F)
```
The script will build the Java code, install the package in an isolated environment
(see [devtools][dt]), and set up the JVM for debugging.
[rj]:http://www.rforge.net/rJava/
[dt]:https://github.com/hadley/devtools
[plm]:https://bitbucket.org/brandonwillard/particlelearningmodels
[dlm]:http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.9040
[fs1]:http://www.sciencedirect.com/science/article/pii/S0167947306003720
[fs2]:http://dl.acm.org/citation.cfm?id=2414419
[pl]:http://projecteuclid.org/euclid.ss/1280841735
[waterfilling]:tbd