Showing 5 of total 5 results (show query)
rconsortium
S7:An Object Oriented System Meant to Become a Successor to S3 and S4
A new object oriented programming system designed to be a successor to S3 and S4. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and the wider R community.
Maintained by Hadley Wickham. Last updated 4 months ago.
440 stars 13.28 score 86 scripts 24 dependents
rkillick
changepoint:Methods for Changepoint Detection
Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
Maintained by Rebecca Killick. Last updated 4 months ago.
133 stars 11.05 score 736 scripts 40 dependents
flr
mse:Tools for Running Management Strategy Evaluations using FLR
A set of functions and methods to enable the development and running of Management Strategy Evaluation (MSE) analyses, using the FLR packages and classes and the a4a methods and algorithms.
Maintained by Iago Mosqueira. Last updated 1 months ago.
4 stars 6.99 score 137 scripts 3 dependents
diegommcc
SpatialDDLS:Deconvolution of Spatial Transcriptomics Data Based on Neural Networks
Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
Maintained by Diego Mañanes. Last updated 5 months ago.
deconvolutiondeep-learningneural-networkspatial-transcriptomics
5 stars 4.88 score 1 scripts
bioc
MLSeq:Machine Learning Interface for RNA-Seq Data
This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.
Maintained by Gokmen Zararsiz. Last updated 5 months ago.
immunooncologysequencingrnaseqclassificationclustering
4.81 score 27 scripts 1 dependents