CRAN
nimble 0.8.0
MCMC, Particle Filtering, and Programmable Hierarchical Modeling
Released Jun 2, 2019 by Christopher Paciorek
Dependencies
igraph 1.2.4.1 coda 0.19-2 R6 2.4.0
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at
Installation
Maven
This package can be included as a dependency from a Java or Scala project by including
the following your project's pom.xml
file.
Read more
about embedding Renjin in JVM-based projects.
<dependencies> <dependency> <groupId>org.renjin.cran</groupId> <artifactId>nimble</artifactId> <version>0.8.0-b1</version> </dependency> </dependencies> <repositories> <repository> <id>bedatadriven</id> <name>bedatadriven public repo</name> <url>https://nexus.bedatadriven.com/content/groups/public/</url> </repository> </repositories>
Renjin CLI
If you're using Renjin from the command line, you load this library by invoking:
library('org.renjin.cran:nimble')
Test Results
This package was last tested against Renjin 0.9.2726 on Jul 13, 2019.
- CAR-Normal-examples
- CAR-Proper-examples
- Categorical-examples
- ChineseRestaurantProcess-examples
- Constraint-examples
- Dirichlet-examples
- Double-Exponential-examples
- Exponential-examples
- Interval-examples
- Inverse-Gamma-examples
- Inverse-Wishart-examples
- MCMCconf-class-examples
- Multinomial-examples
- Multivariate-t-examples
- MultivariateNormal-examples
- StickBreakingFunction-examples
- Wishart-examples
- buildMCEM-examples
- declare-examples
- distributionInfo-examples
- flat-examples
- getNimbleOption-examples
- getsize-examples
- identityMatrix-examples
- initializeModel-examples
- modelBaseClass-class-examples
- modelValues-examples
- modelValuesBaseClass-class-examples
- modelValuesConf-examples
- model_macro_builder-examples
- nfVar-examples
- nimCat-examples
- nimCopy-examples
- nimDim-examples
- nimEigen-examples
- nimPrint-examples
- nimSvd-examples
- nimbleCode-examples
- nimbleList-examples
- nimbleModel-examples
- nimbleOptions-examples
- nimbleType-class-examples
- rankSample-examples
- readBUGSmodel-examples
- registerDistributions-examples
- resize-examples
- samplers-examples
- setAndCalculate-examples
- setAndCalculateOne-examples
- simNodesMV-examples
- t-examples