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easystats
parameters:Processing of Model Parameters
Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Maintained by Daniel Lüdecke. Last updated 10 days ago.
betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models
454 stars 15.67 score 1.8k scripts 56 dependents
bioc
SingleCellSignalR:Cell Signalling Using Single Cell RNAseq Data Analysis
Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions.
Maintained by Jacques Colinge Developer. Last updated 5 months ago.
singlecellnetworkclusteringrnaseqclassification
5.87 score 35 scripts 1 dependentsbioc
multiClust:multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles
Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.
Maintained by Nathan Lawlor. Last updated 5 months ago.
featureextractionclusteringgeneexpressionsurvival
4.34 score 11 scripts