Package: FuncNN 1.0
FuncNN: Functional Neural Networks
A collection of functions which fit functional neural network models. In other words, this package will allow users to build deep learning models that have either functional or scalar responses paired with functional and scalar covariates. We implement the theoretical discussion found in Thind, Multani and Cao (2020) <arxiv:2006.09590> through the help of a main fitting and prediction function as well as a number of helper functions to assist with cross-validation, tuning, and the display of estimated functional weights.
Authors:
FuncNN_1.0.tar.gz
FuncNN_1.0.zip(r-4.5)FuncNN_1.0.zip(r-4.4)FuncNN_1.0.zip(r-4.3)
FuncNN_1.0.tgz(r-4.4-any)FuncNN_1.0.tgz(r-4.3-any)
FuncNN_1.0.tar.gz(r-4.5-noble)FuncNN_1.0.tar.gz(r-4.4-noble)
FuncNN_1.0.tgz(r-4.4-emscripten)FuncNN_1.0.tgz(r-4.3-emscripten)
FuncNN.pdf |FuncNN.html✨
FuncNN/json (API)
# Install 'FuncNN' in R: |
install.packages('FuncNN', repos = c('https://b-thi.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/b-thi/funcnn/issues
Last updated 4 years agofrom:dd44b58680. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 06 2024 |
R-4.5-win | NOTE | Nov 06 2024 |
R-4.5-linux | NOTE | Nov 06 2024 |
R-4.4-win | NOTE | Nov 06 2024 |
R-4.4-mac | NOTE | Nov 06 2024 |
R-4.3-win | NOTE | Nov 06 2024 |
R-4.3-mac | NOTE | Nov 06 2024 |
Exports:fnn.cvfnn.fitfnn.fncfnn.plotfnn.predictfnn.tune
Dependencies:abindashbackportsbase64encbitopsbootbroomcarcarDatacaretcaToolsclasscliclockclustercodetoolscolorspaceconfigcorrplotcowplotcpp11data.tableDerivdeSolvediagramdigestdoBydoParalleldplyre1071fansifarverfdafda.uscfdsfluxFNNforeachFormulafuturefuture.applygenericsggplot2ggpubrggrepelggsciggsignifglobalsgluegowergridExtragtablehardhathdrcdehereipredisobanditeratorsjsonlitekeraskernlabKernSmoothkskSampleslabelinglatticelavalifecyclelistenvlme4locfitlubridatemagrittrMASSMatrixMatrixModelsmclustmgcvmicrobenchmarkminqaModelMetricsmodelrmulticoolmunsellmvtnormnlmenloptrnnetnumDerivparallellypbapplypbkrtestpcaPPpillarpkgconfigplyrpngpolynompracmapROCprocessxprodlimprogressrproxypspurrrquantregR6rainbowrappdirsRColorBrewerRcppRcppEigenRcppTOMLRCurlrecipesreshape2reticulaterlangrpartrprojrootrstatixrstudioapiscalesshapeSparseMSQUAREMstringistringrSuppDistssurvivaltensorflowtfautographtfrunstibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewhiskerwithryamlzeallot
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Classic Canadian weather data set. | daily |
Functional Neural Networks with Cross-validation | fnn.cv |
Fitting Functional Neural Networks | fnn.fit |
Output of Estimated Functional Weights | fnn.fnc |
Plotting Functional Response Predictions | fnn.plot |
Prediction using Functional Neural Networks | fnn.predict |
Tuning Functional Neural Networks | fnn.tune |
Classic Tecator data set. | tecator |