Showing 13 of total 13 results (show query)
nimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 18 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
169 stars 12.97 score 2.6k scripts 19 dependents
bioc
splatter:Simple Simulation of Single-cell RNA Sequencing Data
Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets.
Maintained by Luke Zappia. Last updated 4 months ago.
singlecellrnaseqtranscriptomicsgeneexpressionsequencingsoftwareimmunooncologybioconductorbioinformaticsscrna-seqsimulation
224 stars 9.92 score 424 scripts 1 dependents
bioc
CoGAPS:Coordinated Gene Activity in Pattern Sets
Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.
Maintained by Elana J. Fertig. Last updated 18 days ago.
geneexpressiontranscriptiongenesetenrichmentdifferentialexpressionbayesianclusteringtimecoursernaseqmicroarraymultiplecomparisondimensionreductionimmunooncologycpp
6.97 score 104 scripts
modal-inria
RMixtCompUtilities:Utility Functions for 'MixtComp' Outputs
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility functions to facilitate the analysis of 'MixtComp' output.
Maintained by Quentin Grimonprez. Last updated 11 months ago.
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
13 stars 5.19 score 2 scripts 1 dependentsbioc
rCGH:Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data
A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz.
Maintained by Frederic Commo. Last updated 5 months ago.
acghcopynumbervariationpreprocessingfeatureextraction
4 stars 5.10 score 26 scripts 1 dependentsbioc
tweeDEseq:RNA-seq data analysis using the Poisson-Tweedie family of distributions
Differential expression analysis of RNA-seq using the Poisson-Tweedie (PT) family of distributions. PT distributions are described by a mean, a dispersion and a shape parameter and include Poisson and NB distributions, among others, as particular cases. An important feature of this family is that, while the Negative Binomial (NB) distribution only allows a quadratic mean-variance relationship, the PT distributions generalizes this relationship to any orde.
Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.
immunooncologystatisticalmethoddifferentialexpressionsequencingrnaseqdnaseq
4.91 score 45 scripts 1 dependents
husson
FactoInvestigate:Automatic Description of Factorial Analysis
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
Maintained by Francois Husson. Last updated 2 years ago.
1 stars 4.90 score 68 scripts 1 dependents
jonasrieger
ldaPrototype:Prototype of Multiple Latent Dirichlet Allocation Runs
Determine a Prototype from a number of runs of Latent Dirichlet Allocation (LDA) measuring its similarities with S-CLOP: A procedure to select the LDA run with highest mean pairwise similarity, which is measured by S-CLOP (Similarity of multiple sets by Clustering with Local Pruning), to all other runs. LDA runs are specified by its assignments leading to estimators for distribution parameters. Repeated runs lead to different results, which we encounter by choosing the most representative LDA run as prototype.
Maintained by Jonas Rieger. Last updated 2 years ago.
latent-dirichlet-allocationldamodel-selectionmodelselectionreliabilitytext-miningtextdatatopic-modeltopic-modelstopic-similaritiestopicmodelingtopicmodelling
8 stars 4.44 score 23 scripts 1 dependents
beerda
rmake:Makefile Generator for R Analytical Projects
Creates and maintains a build process for complex analytic tasks in R. Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process by (in parallel) executing build commands in order to update results accordingly to given dependencies on changed data or updated source files.
Maintained by Michal Burda. Last updated 3 years ago.
1 stars 4.11 score 26 scriptsbioc
pipeFrame:Pipeline framework for bioinformatics in R
pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts.
Maintained by Zheng Wei. Last updated 5 months ago.
softwareinfrastructureworkflowstep
1 stars 4.08 score 2 scripts 1 dependentsbioc
scTensor:Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition
The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor.
Maintained by Koki Tsuyuzaki. Last updated 5 months ago.
dimensionreductionsinglecellsoftwaregeneexpression
4.00 score 2 scripts
jamescbell
gestate:Generalised Survival Trial Assessment Tool Environment
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
Maintained by James Bell. Last updated 2 years ago.
2 stars 2.60 score 8 scripts