Imports: coda, ggplot2, plot3D, threejs, gridExtra, cubature, Formula,

Description: Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab priors for variable selection mean/variance regression models, for univariate and multivariate response models.

Description: MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) response analysis, with nonparametric models for the means, the variances and the correlation matrix.

Description: MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix.

Description: MCMC algorithms & processing functions for: 1. multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, and 2. Dirichlet process mixtures.

Description: MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2020), and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.

Title: Pathway Enrichment Analysis Based on Bayesian Network
Authors@R: c(person("Samaneh","Maleknia",role = c("aut","cre"),email = "maleknias@gmail.com"),person("Mohsen","Namazi",role = "aut"),person("Kaveh","Kavousi",role = c("ctb")),person("Ali","Sharifi-Zarchi",role = c("ctb")),person("Vahid","Rezaei-Tabar",role = c("ctb")))
Description: Maleknia et al. (2020) <doi:10.1101/2020.01.13.905448>. A novel pathway enrichment analysis package based on Bayesian network to investigate the topology features of the pathways. firstly, 187 kyoto encyclopedia of genes and genomes (KEGG) human non-metabolic pathways which their cycles were eliminated by biological approach, enter in analysis as Bayesian network structures. The constructed Bayesian network were optimized by the Least Absolute Shrinkage Selector Operator (lasso) and the parameters were learned based on gene expression data. Finally, the impacted pathways were enriched by Fisher’s Exact Test on significant parameters.
biocViews: NetworkEnrichment, GeneExpression, Pathways, Bayesian, KEGG
Imports: bnlearn, corpcor, glmnet, graph, stats, utils
Packaged: 2020-03-16 08:43:03 UTC; Shawshank