CRAN
MoTBFs 1.0
Learning Hybrid Bayesian Networks using Mixtures of Truncated Basis Functions
Released Sep 28, 2015 by Inmaculada Pérez-Bernabé
Dependencies
bnlearn 4.3 quadprog 1.5-5 lpSolve 5.6.13
Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks. The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called 'motbf' and 'jointmotbf'.
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>MoTBFs</artifactId>
<version>1.0-b54</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:MoTBFs')
Test Results
This package was last tested against Renjin 0.9.2689 on Aug 27, 2018.
- BICMoTBF-examples
- BICMultiFunctions-examples
- Class-JointMoTBF-examples
- Class-MoTBF-examples
- LearningHC-examples
- MoTBF-Distribution-examples
- MoTBFs_Learning-examples
- Subclass-MoTBF-examples
- UpperBoundLogLikelihood-examples
- as.function.jointmotbf-examples
- as.function.motbf-examples
- asMOPString-examples
- asMTEString-examples
- clean-examples
- coef.jointmotbf-examples
- coef.mop-examples
- coef.motbf-examples
- coef.mte-examples
- coefExpJointCDF-examples
- conditionalmotbf.learning-examples
- dataMining-examples
- derivMOP-examples
- derivMTE-examples
- derivMoTBF-examples
- dimensionFunction-examples
- evalJointFunction-examples
- generateNormalPriorData-examples
- getChildParentsFromGraph-examples
- getCoefficients-examples
- getNonNormalisedRandomMoTBF-examples
- goodnessDiscreteVariables-examples
- goodnessMoTBFBN-examples
- integralJointMoTBF-examples
- integralMOP-examples
- integralMTE-examples
- integralMoTBF-examples
- jointCDF-examples
- jointmotbf.learning-examples
- learnMoTBFpriorInformation-examples
- marginalJointMoTBF-examples
- mop.learning-examples
- mte.learning-examples
- nVariables-examples
- newRangePriorData-examples
- plot.jointmotbf-examples
- plot.motbf-examples
- plotConditional-examples
- printBN-examples
- printConditional-examples
- probDiscreteVariable-examples
- rescaledFunctions-examples
- rnormMultiv-examples
- subsetData-examples
- summary.jointmotbf-examples
- summary.motbf-examples
- univMoTBF-examples