
SIMoNe
 Referenced in 6 articles
[sw06067]
 simone: Statistical Inference for MOdular NEtworks (SIMoNe) , The R package simone implements the inference...

gergm
 Referenced in 3 articles
[sw21317]
 using available methods for statistical inference with networks. The generalized exponential random graph model (GERGM...

bc3net
 Referenced in 1 article
[sw17708]
 Simoes and Frank EmmertStreib, Bagging Statistical Network Inference from LargeScale Gene Expression Data...

Tuffy
 Referenced in 10 articles
[sw28901]
 Markov Logic Network inference engine, and part of Felix. Markov Logic Networks (MLNs ... powerful framework that combines statistical and logical reasoning; they have been applied to many data ... data management techniques, Tuffy is an MLN inference engine that achieves scalability and orders...

Alchemy
 Referenced in 10 articles
[sw16040]
 series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov ... Collective classification; Link prediction; Entity resolution; Social network modeling; Information extraction...

minet
 Referenced in 6 articles
[sw08432]
 functions to infer mutual information networks from a dataset. Once fed with a microarray dataset ... package returns a network where nodes denote genes, edges model statistical dependencies between genes ... weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene ... well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also...

netgwas
 Referenced in 3 articles
[sw21512]
 netgwas: An R Package for NetworkBased GenomeWide Association Studies. Graphical models provide powerful ... tools to model and make the statistical inference regarding complex relationships among variables in multivariate ... widely used in statistics and machine learning particularly to analyze biological networks. In this paper ... 2017b), and in inferring the conditional independence network for nonGaussian, discrete, and mixed data...

LSSVMlab
 Referenced in 23 articles
[sw07367]
 been introduced within the context of statistical learning theory and structural risk minimization ... SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit ... analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness, sparseness ... Bayesian framework with three levels of inference has been developed. LSSVM based primaldual...

FACTORIE
 Referenced in 12 articles
[sw08947]
 model structure, inference, and learning. By combining the traditional, declarative, statistical semantics of factor graphs ... language. In experimental comparisons to Markov Logic Networks on joint segmentation and coreference, we find...

Graph_sampler
 Referenced in 1 article
[sw20652]
 Bayesian networks (BNs) are widely used graphical models usable to draw statistical inference about Directed ... fast free C language software for structural inference on BNs. Graph_sampler uses a fully ... data and prior information about the network structure are considered. This new software can handle...

latentnet
 Referenced in 12 articles
[sw10550]
 evaluate statistical latent position and cluster models for networks. Hoff, Raftery, and Handcock (2002) suggested ... approach to modeling networks based on positing the existence of an latent space of characteristics ... Tantrum (2007). The package implements Bayesian inference for the models based on an Markov chain...

PANFIS
 Referenced in 4 articles
[sw13735]
 novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this ... stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected...

HCCVis
 Referenced in 2 articles
[sw30710]
 Coherencebased time series clustering for statistical inference and visualization of brain connectivity. We develop ... procedure for characterizing connectivity in a network by clustering nodes or groups of channels that...

KReator
 Referenced in 3 articles
[sw06946]
 programming (or statistical relational learning) aims at applying probabilistic methods of inference and learning ... probabilistic methods like Bayes Nets and Markov Networks on relational settings. Only few developers provide ... area of probabilistic inductive logic programming and statistical relational learning. Currently, KReator implements Bayesian logic...

RHugin
 Referenced in 3 articles
[sw16347]
 building and making inference from Bayesian belief networks. The RHugin package provides a suite ... thus be used to build Bayesian belief networks, enter and propagate evidence, and to retrieve ... would like to take advantage of the statistical and programatic capabilities of R. Please note...

matLeap
 Referenced in 1 article
[sw16549]
 suitable for Bayesian inference. Background: Species abundance distributions in chemical reaction network models cannot usually ... leaping, and iii) provides summary statistics necessary for Bayesian inference. Results: We provide a Matlab...

MMG
 Referenced in 4 articles
[sw29328]
 conditions. Popular approaches involve using ttest statistics, based on modelling the data as arising ... usually related through a complex (weighted) network of interactions, and often the more pertinent question ... networks and pathways. The method can easily incorporate information about weights in the network ... generalized to directed networks. We propose an efficient sampling strategy to infer posterior probabilities...

LNEMLC
 Referenced in 1 article
[sw28400]
 label network and uses it to extend input space in learning and inference ... literature. We demonstrate how the method reveals statistically significant improvements over the simple kNN baseline...

LNA++
 Referenced in 1 article
[sw27627]
 approximate description of the statistical moments of stochastic chemical reaction networks, a commonly used modeling ... CRNs makes it a common choice for inference of unknown chemical reaction parameters. However, this...

TRANSWESD
 Referenced in 1 article
[sw17199]
 TRANSWESD: inferring cellular networks with transitive reduction. Motivation: Distinguishing direct from indirect influences ... central issue in reverse engineering of biological networks because it facilitates detection and removal ... Major changes and improvements concern: (i) new statistical approaches for generating highquality perturbation graphs...