Showing 20 of total 20 results (show query)
hojsgaard
gRain:Bayesian Networks
Probability propagation in Bayesian networks, also known as graphical independence networks. Documentation of the package is provided in vignettes included in the package and in the paper by Højsgaard (2012, <doi:10.18637/jss.v046.i10>). See 'citation("gRain")' for details.
Maintained by Søren Højsgaard. Last updated 5 months ago.
2 stars 9.13 score 408 scripts 8 dependents
bioc
miRspongeR:Identification and analysis of miRNA sponge regulation
This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.
Maintained by Junpeng Zhang. Last updated 5 months ago.
geneexpressionbiomedicalinformaticsnetworkenrichmentsurvivalmicroarraysoftwaresinglecellspatialrnaseqcernamirnasponge
5 stars 5.88 score 8 scriptshojsgaard
gRim:Graphical Interaction Models
Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.
Maintained by Søren Højsgaard. Last updated 6 months ago.
2 stars 5.77 score 74 scripts
cfwp
rags2ridges:Ridge Estimation of Precision Matrices from High-Dimensional Data
Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.
Maintained by Carel F.W. Peeters. Last updated 1 years ago.
c-plus-plusgraphical-modelsmachine-learningnetworksciencestatisticsopenblascpp
8 stars 5.60 score 46 scripts
alesmascaro
BCDAG:Bayesian Structure and Causal Learning of Gaussian Directed Graphs
A collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.
Maintained by Alessandro Mascaro. Last updated 30 days ago.
3 stars 5.58 score 17 scriptsbioc
SPONGE:Sparse Partial Correlations On Gene Expression
This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Maintained by Markus List. Last updated 5 months ago.
geneexpressiontranscriptiongeneregulationnetworkinferencetranscriptomicssystemsbiologyregressionrandomforestmachinelearning
5.36 score 38 scripts 1 dependentsbioc
MOSClip:Multi Omics Survival Clip
Topological pathway analysis tool able to integrate multi-omics data. It finds survival-associated modules or significant modules for two-class analysis. This tool have two main methods: pathway tests and module tests. The latter method allows the user to dig inside the pathways itself.
Maintained by Paolo Martini. Last updated 5 months ago.
softwarestatisticalmethodgraphandnetworksurvivalregressiondimensionreductionpathwaysreactome
5.34 score 5 scriptsbioc
RVS:Computes estimates of the probability of related individuals sharing a rare variant
Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test).
Maintained by Alexandre Bureau. Last updated 5 months ago.
immunooncologygeneticsgenomewideassociationvariantdetectionexomeseqwholegenome
4.78 score 9 scriptsbioc
clipper:Gene Set Analysis Exploiting Pathway Topology
Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
Maintained by Paolo Martini. Last updated 5 months ago.
4.48 score 19 scriptsbioc
fgga:Hierarchical ensemble method based on factor graph
Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.
Maintained by Flavio Spetale. Last updated 5 months ago.
softwarestatisticalmethodclassificationnetworknetworkinferencesupportvectormachinegraphandnetworkgo
3 stars 4.48 score 6 scriptsbioc
GmicR:Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC)
This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool.
Maintained by Richard Virgen-Slane. Last updated 5 months ago.
softwaresystemsbiologygraphandnetworknetworknetworkinferenceguiimmunooncologygeneexpressionqualitycontrolbayesianclustering
4.00 score 2 scripts
manueleleonelli
bnmonitor:An Implementation of Sensitivity Analysis in Bayesian Networks
An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.
Maintained by Manuele Leonelli. Last updated 6 months ago.
3 stars 3.92 score 14 scripts
spaceodyssey
cia:Learn and Apply Directed Acyclic Graphs for Causal Inference
Causal Inference Assistance (CIA) for performing causal inference within the structural causal modelling framework. Structure learning is performed using partition Markov chain Monte Carlo (Kuipers & Moffa, 2017) and several additional functions have been added to help with causal inference. Kuipers and Moffa (2017) <doi:10.1080/01621459.2015.1133426>.
Maintained by Mathew Varidel. Last updated 3 months ago.
3.85 score 5 scripts
nystat
OrdCD:Ordinal Causal Discovery
Algorithms for ordinal causal discovery. This package aims to enable users to discover causality for observational ordinal categorical data with greedy and exhaustive search. See Ni, Y., & Mallick, B. (2022) <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal Causal Discovery. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, (UAI 2022), PMLR 180:1530–1540".
Maintained by Yang Ni. Last updated 2 years ago.
2.70 scorehojsgaard
gRc:Inference in Graphical Gaussian Models with Edge and Vertex Symmetries
Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about 'gRc' is provided in the paper by Hojsgaard and Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).
Maintained by Søren Højsgaard. Last updated 6 months ago.
2.60 score 5 scripts
petergreen5678
gRaven:Bayes Nets: 'RHugin' Emulation with 'gRain'
Wrappers for functions in the 'gRain' package to emulate some 'RHugin' functionality, allowing the building of Bayesian networks consisting on discrete chance nodes incrementally, through adding nodes, edges and conditional probability tables, the setting of evidence, both 'hard' (boolean) or 'soft' (likelihoods), querying marginal probabilities and normalizing constants, and generating sets of high-probability configurations. Computations will typically not be so fast as they are with 'RHugin', but this package should assist users without access to 'Hugin' to use code written to use 'RHugin'.
Maintained by Peter Green. Last updated 6 months ago.
2.48 score 2 scripts 2 dependents
cran
topologyGSA:Gene Set Analysis Exploiting Pathway Topology
Using Gaussian graphical models we propose a novel approach to perform pathway analysis using gene expression. Given the structure of a graph (a pathway) we introduce two statistical tests to compare the mean and the concentration matrices between two groups. Specifically, these tests can be performed on the graph and on its connected components (cliques). The package is based on the method described in Massa M.S., Chiogna M., Romualdi C. (2010) <doi:10.1186/1752-0509-4-121>.
Maintained by Gabriele Sales. Last updated 2 years ago.
1.60 score
tgraversen
DNAmixturesLite:Statistical Inference for Mixed Traces of DNA (Lite-Version)
Statistical methods for DNA mixture analysis. This package is a lite-version of the 'DNAmixtures' package to allow users without a 'HUGIN' software license to experiment with the statistical methodology. While the lite-version aims to provide the full functionality it is noticeably less efficient than the original 'DNAmixtures' package. For details on implementation and methodology see <https://dnamixtures.r-forge.r-project.org/>.
Maintained by Therese Graversen. Last updated 2 years ago.
1.48 score 1 dependentspetergreen5678
KinMixLite:Inference About Relationships from DNA Mixtures
Methods for inference about relationships between contributors to a DNA mixture and other individuals of known genotype: a basic example would be testing whether a contributor to a mixture is the father of a child of known genotype. This provides most of the functionality of the 'KinMix' package, but with some loss of efficiency and restriction on problem size, as the latter uses 'RHugin' as the Bayes net engine, while this package uses 'gRain'. The package implements the methods introduced in Green, P. J. and Mortera, J. (2017) <doi:10.1016/j.fsigen.2017.02.001> and Green, P. J. and Mortera, J. (2021) <doi:10.1111/rssc.12498>.
Maintained by Peter Green. Last updated 6 months ago.
1.00 score