Showing 8 of total 8 results (show query)
furrer-lab
abn:Modelling Multivariate Data with Additive Bayesian Networks
The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.
Maintained by Matteo Delucchi. Last updated 22 days ago.
bayesian-networkbinomialcategorical-datagaussiangrouped-datasetsmixed-effectsmultinomialmultivariatepoissonstructure-learninggslopenblascppopenmpjags
6 stars 6.88 score 90 scripts
cbg-ethz
clustNet:Network-Based Clustering
Network-based clustering using a Bayesian network mixture model with optional covariate adjustment.
Maintained by Fritz Bayer. Last updated 1 years ago.
bayesian-networkbayesian-networksclusteringdaggenomicsmixture-modelnetwork-clustering
7 stars 5.16 score 41 scripts
ralmond
CPTtools:Tools for Creating Conditional Probability Tables
Provides support parameterized tables for Bayesian networks, particularly the IRT-like DiBello tables. Also, provides some tools for visualing the networks.
Maintained by Russell Almond. Last updated 3 months ago.
1 stars 5.05 score 21 scripts 4 dependentsralmond
RNetica:R interface to Netica(R) Bayesian Network Engine
This provides an R interface to the Netica (http://norsys.com/) Bayesian network library API.
Maintained by Russell Almond. Last updated 3 months ago.
2 stars 4.92 score 14 scripts 2 dependents
robson-fernandes
bnviewer:Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence
Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The 'bnviewer' is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.
Maintained by Robson Fernandes. Last updated 5 years ago.
bayesian-inferencebayesian-networkbayesian-networksprobabilistic-graphical-models
7 stars 4.86 score 69 scripts 1 dependentsralmond
Peanut:Parameterized Bayesian Networks, Abstract Classes
This provides support of learning conditional probability tables parameterized using CPTtools. This provides and object oriented layer on top of a CPTtools, to facilitate calculations with Parameterized models for Bayesian networks. Peanut is a collection of abstract classes and generic functions defining a protocol, with the intent that the protocol can be implemented with different Bayes net engines. The companion pacakge PNetica provides an implementation using Netica and RNetica.
Maintained by Russell Almond. Last updated 2 years ago.
bayesian-networkknowledge-representation
1 stars 2.48 score 4 scripts 2 dependentsralmond
PNetica:Parameterized Bayesian Networks Netica Interface
This package provides RNetica implementation of Peanut interface.This provides an implementation of the Peanut protocol using the Netica (RNetica) Bayesian network engine. This allows parameters of parametric Bayesian network models written and Netica and using Peanut to be processed with R tools, and the parameter saved in the Netica objects.
Maintained by Russell Almond. Last updated 2 years ago.
1 stars 1.90 score 16 scriptsralmond
EABN:Evidence Accumulation Bayes Net Engine
Extracts observables from a sequence of events.
Maintained by Russell Almond. Last updated 2 years ago.
assessment-scoringbayesian-networkevidence-centered-design
1.00 score 3 scripts