Package: rSFA 1.5
rSFA: Slow Feature Analysis
Slow Feature Analysis (SFA), ported to R based on 'matlab' implementations of SFA: 'SFA toolkit' 1.0 by Pietro Berkes and 'SFA toolkit' 2.8 by Wolfgang Konen.
Authors:
rSFA_1.5.tar.gz
rSFA_1.5.zip(r-4.5)rSFA_1.5.zip(r-4.4)rSFA_1.5.zip(r-4.3)
rSFA_1.5.tgz(r-4.4-any)rSFA_1.5.tgz(r-4.3-any)
rSFA_1.5.tar.gz(r-4.5-noble)rSFA_1.5.tar.gz(r-4.4-noble)
rSFA_1.5.tgz(r-4.4-emscripten)rSFA_1.5.tgz(r-4.3-emscripten)
rSFA.pdf |rSFA.html✨
rSFA/json (API)
NEWS
| # Install 'rSFA' in R: |
| install.packages('rSFA', repos = c('https://martinzaefferer.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:c8faff4caa. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 12 2024 |
| R-4.5-win | OK | Nov 12 2024 |
| R-4.5-linux | OK | Nov 12 2024 |
| R-4.4-win | OK | Nov 12 2024 |
| R-4.4-mac | OK | Nov 12 2024 |
| R-4.3-win | OK | Nov 12 2024 |
| R-4.3-mac | OK | Nov 12 2024 |
Exports:addNoisyCopiescustomRepmatcustomSizeetavalgaussClassifiergaussCreategaussLoadgaussSavelcovCreatelcovFixlcovPcalcovPca2lcovTransformlcovUpdatenlDimnlExpandsfa1sfa1Createsfa1Stepsfa2sfa2Createsfa2StepsfaBShsfaClassifysfaClassPredictsfaExecutesfaExpandsfaLoadsfaNlRegresssfaPBootstrapsfaPreprocsfaSavesfaStepsfaTimediffxpDim
Dependencies:MASS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Slow Feature Analysis | rSFA-package rSFA |
| Add noisy copies for parametric bootstrap | addNoisyCopies |
| Computes the eta value of a signal (slowness) | etaval |
| Classifier for SFA demos | gaussClassifier |
| Create an Gaussian classifier object | gaussCreate |
| The SFA1 algorithm, linear SFA. | sfa1 |
| Create structured list for linear SFA | sfa1Create |
| The SFA2 algorithm, SFA with degree 2 expansion. | sfa2 |
| Create structured list for expanded SFA | sfa2Create |
| Predict Class for SFA classification | sfaClassify |
| Predict Class for SFA classification | sfaClassPredict |
| Execute learned function for input data | sfaExecute |
| Degree 2 Expansion | sfaExpand |
| Perform non-linear regression | sfaNlRegress |
| Parametric Bootstrap | sfaPBootstrap |
| Update a step of the SFA algorithm. | sfaStep |
| Calculates the first derivative of signal data | sfaTimediff |
| Degree 2 Dimension Calculation | xpDim |
