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FMradio

The R-package FMradio supports stable prediction and classification with radiomics data through factor-analytic modeling. This support can be invoked irrespective of the imaging modality (such as, e.g., MRI, PET, CT) used to produce the radiomics data.

Installation

If you wish to install the latest version of FMradio directly from the master branch here at GitHub, run

#install.packages("devtools")  # Uncomment if devtools is not installed
devtools::install_github("CFWP/FMradio")

Be sure that you have the package development prerequisites if you wish to install the package from the source.

Manual and other documentation

  • A pdf-version of the manual can be found here.

  • The R-script used to produce the simulations and results contained in Peeters, C.F.W., et al. (2019) [see references below] can be found here.

References

Relevant publications to FMradio include:

  1. Peeters, C.F.W. (2019). "FMradio: Factor modeling for radiomic data". R package, version 1.1
  2. Peeters, C.F.W., et al. (2019) "Stable prediction with radiomics data". arXiv:1903.11696 [stat.ML]

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Version

Install

install.packages('FMradio')

Monthly Downloads

228

Version

1.1.1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

Carel FW Peeters

Last Published

December 16th, 2019

Functions in FMradio (1.1.1)

dimLRT

Assess the latent dimensionality using a likelihood ratio test
SA

Calculate the KMO measure of feature-sampling adequacy
SMC

Compare squared multiple correlations with model-based communalities
dimIC

Assess the latent dimensionality using information criteria
autoFMradio

Wrapper for automated workflow
RF

Redundancy filtering of a square (correlation) matrix
dimGB

Assess the latent dimensionality using Guttman bounds
dimVAR

Assessing variances under factor solutions
FAsim

Simulate data according to the common factor analytic model
FMradio-package

Factor modeling for radiomic data
regcor

Regularized correlation matrix estimation
subSet

Subset a data matrix or expression set
radioHeat

Visualize a (correlation) matrix as a heatmap
mlFA

Maximum likelihood factor analysis
facSMC

Evaluate the determinacy of factor scores
facScore

Compute factor scores