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dplbnDE
View on CRAN: Click
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Download and install dplbnDE package within the R console
Install from CRAN:
install.packages("dplbnDE")
Install from Github:
library("remotes")
install_github("cran/dplbnDE")
Install by package version:
library("remotes")
install_version("dplbnDE", "0.1.3")
Attach the package and use:
library("dplbnDE")
Maintained by
Alejandro Platas-Lopez
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
First Published: 2022-01-10
Latest Update: 2023-08-18
Description:
Implements Differential Evolution (DE) to train parameters of Bayesian Networks for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given Bayesian Network structure encodes assumptions about conditional independencies among the attributes and will result in an error if they do not hold in the data. Such an error includes the classification dimension. The main goal of Discriminative learning is to minimize this type of error. This package provides main variants of differential evolution described in Price & Storn (1996) and recent ones, described in Tanabe & Fukunaga (2014) and Zhang & Sanderson (2009) with adaptation mechanism for factor mutarion and crossover rate.
How to cite:
Alejandro Platas-Lopez (2022). dplbnDE: Discriminative Parameter Learning of Bayesian Networks by
Differential Evolution. R package version 0.1.3, https://cran.r-project.org/web/packages/dplbnDE. Accessed 24 Apr. 2025.
Previous versions and publish date:
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Other R packages that dplbnDE depends,
imports, suggests or enhances
Functions, R codes and Examples using
the dplbnDE R package
Some associated functions: DE . DEbest . DErand . basGen . car . dplbnDE . getPBest . individual . jade . keepSum . lev2 . lev3 . lshade . meanL . meanWL . parGen . plot.DE . population . print.DE . randC . randN . reflect . strRep . vec . vec2net .
Some associated R codes: 0dplbnDE-doc.R . DEbest.R . DErand.R . jade.R . lshade.R . methods.R . utils.R . Full dplbnDE package functions and examples
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