• 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 Emmert-Streib, Bagging Statistical Network Inference from Large-Scale Gene Expression Data...
  • Tuffy

  • Referenced in 9 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...
  • minet

  • Referenced in 7 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...
  • 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...
  • netgwas

  • Referenced in 3 articles [sw21512]
  • netgwas: An R Package for Network-Based Genome-Wide 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 non-Gaussian, discrete, and mixed data...
  • LS-SVMlab

  • Referenced in 24 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. LS-SVM based primal-dual...
  • 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 13 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 5 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...
  • HCC-Vis

  • Referenced in 2 articles [sw30710]
  • Coherence-based 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 t-test 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...
  • BLNN

  • Referenced in 1 article [sw32579]
  • neural networks using Bayesian inference. The Bayesian Learning for Neural Networks (BLNN) package coalesces ... predictive power of neural networks with a breadth of Bayesian sampling techniques for the first ... applications which are based on developing statistical models such as multiple linear and logistic regression...
  • 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...