| Functional Data Analysis and Utilities for Statistical Computing (fda.usc) | fda.usc-package fda.usc |
| Performance measures for regression and classification models | accuracy cat2meas pred.MAE pred.MSE pred.RMSE pred2meas pred2meas. tab2meas |
| aemet data | aemet |
| DD-Classifier Based on DD-plot | classif.DD |
| Classifier from Functional Data | classif.depth |
| Classification Fitting Functional Generalized Kernel Additive Models | classif.gkam |
| Classification Fitting Functional Generalized Linear Models | classif.glm |
| Classification Fitting Functional Generalized Additive Models | classif.gsam |
| Variable Selection in Functional Data Classification | classif.gsam.vs |
| Functional Classification usign k-fold CV | classif.kfold |
| Functional classification using ML algotithms | classif.cv.glmnet classif.gbm classif.ksvm classif.lda classif.ML classif.multinom classif.naiveBayes classif.nnet classif.qda classif.randomForest classif.rpart classif.svm |
| Kernel Classifier from Functional Data | classif.kernel classif.knn classif.np |
| Conditional Distribution Function | cond.F |
| Conditional mode | cond.mode |
| Conditional quantile | cond.quantile |
| Create Basis Set for Functional Data of fdata class | create.fdata.basis create.pc.basis create.pls.basis create.raw.fdata |
| The cross-validation (CV) score | CV.S |
| Distance Correlation Statistic and t-Test | bcdcor.dist dcor.dist dcor.test dcor.xy |
| Computation of depth measures for functional data | Depth depth.fdata depth.FM depth.FSD depth.KFSD depth.mode depth.RP depth.RPD depth.RT |
| Provides the depth measure for multivariate data | depth.mdata Depth.Multivariate mdepth.FM mdepth.FSD mdepth.HS mdepth.KFSD mdepth.LD mdepth.MhD mdepth.RP mdepth.SD mdepth.TD |
| Provides the depth measure for a list of p-functional data objects | depth.FMp depth.mfdata depth.modep depth.RPp |
| Descriptive measures for functional data. | Descriptive func.mean func.mean.formula func.med.FM func.med.mode func.med.RP func.med.RPD func.med.RT func.trim.FM func.trim.mode func.trim.RP func.trim.RPD func.trim.RT func.trimvar.FM func.trimvar.mode func.trimvar.RP func.trimvar.RPD func.trimvar.RT func.var |
| The deviance score | dev.S |
| Delsol, Ferraty and Vieu test for no functional-scalar interaction | dfv.statistic dfv.test |
| Proximities between functional data | dis.cos.cor |
| ANOVA for heteroscedastic data | anova.hetero fanova.hetero |
| One-way anova model for functional data | anova.onefactor fanova.onefactor |
| Functional ANOVA with Random Project. | anova.RPm fanova.RPm summary.fanova.RPm |
| fda.usc internal functions | !=.fdata *.fdata +.fdata -.fdata /.fdata ==.fdata anyNA.fdata argvals argvals.equi c.fdata colnames.fdata count.na.fdata dim.fdata fda.usc.internal fdata.trace is.na.fdata length.fdata missing.fdata NCOL.fdata ncol.fdata NROW.fdata nrow.fdata omit.fdata omit2.fdata rangeval rownames.fdata trace.matrix unlist_fdata [.fdata [.fdist ^.fdata |
| Converts raw data or other functional data classes into fdata class. | fdata |
| Bootstrap samples of a functional statistic | fdata.bootstrap fdata.bootstrap2 |
| Functional data centred (subtract the mean of each discretization point) | fdata.cen |
| Computes the derivative of functional data object. | fdata.deriv |
| fdata S3 Group Generic Functions | fdata.methods is.fdata Math.fdata Ops.fdata order.fdata split.fdata Summary.fdata |
| Compute fucntional coefficients from functional data represented in a base of functions | fdata2basis summary.basis.fdata |
| Converts fdata class object into fd class object | fdata2fd |
| Principal components for functional data | fdata2pc |
| Partial least squares components for functional data. | fdata2pls |
| False Discorvery Rate (FDR) | FDR pvalue.FDR |
| Tests for checking the equality of distributions between two functional populations. | fEqDistrib.test MMD.test MMDA.test XYRP.test |
| Tests for checking the equality of means and/or covariance between two populations under gaussianity. | cov.test.fdata fEqMoments.test fmean.test.fdata |
| F-test for the Functional Linear Model with scalar response | flm.Ftest Ftest.statistic |
| Goodness-of-fit test for the Functional Linear Model with scalar response | flm.test |
| Functional Regression with scalar response using basis representation. | fregre.basis |
| Cross-validation Functional Regression with scalar response using basis representation. | fregre.basis.cv |
| Functional Regression with functional response using basis representation. | fregre.basis.fr |
| Bootstrap regression | fregre.bootstrap fregre.bootstrap2 |
| Fitting Functional Generalized Kernel Additive Models. | fregre.gkam |
| Fitting Functional Generalized Linear Models | fregre.glm |
| Variable Selection using Functional Linear Models | fregre.glm.vs |
| Fit Functional Linear Model Using Generalized Least Squares | fregre.gls |
| Fitting Functional Generalized Spectral Additive Models | fregre.gsam |
| Variable Selection using Functional Additive Models | fregre.gsam.vs |
| Fit of Functional Generalized Least Squares Model Iteratively | fregre.igls |
| Fitting Functional Linear Models | fregre.lm |
| Functional regression with scalar response using non-parametric kernel estimation | fregre.np |
| Cross-validation functional regression with scalar response using kernel estimation. | fregre.np.cv |
| Functional Regression with scalar response using Principal Components Analysis | fregre.pc |
| Functional penalized PC regression with scalar response using selection of number of PC components | fregre.pc.cv |
| Semi-functional partially linear model with scalar response. | fregre.plm |
| Functional Penalized PLS regression with scalar response | fregre.pls |
| Functional penalized PLS regression with scalar response using selection of number of PLS components | fregre.pls.cv |
| The generalized correlated cross-validation (GCCV) score. | GCCV.S |
| The generalized correlated cross-validation (GCCV) score | GCV.S |
| Calculation of the smoothing parameter (h) for a functional data | h.default |
| Quantile for influence measures | influence_quan |
| Functional influence measures | influence.fregre.fd |
| Inner products of Functional Data Objects o class (fdata) | inprod.fdata |
| Simpson integration | int.simpson int.simpson2 |
| Symmetric Smoothing Kernels. | Ker.cos Ker.epa Ker.norm Ker.quar Ker.tri Ker.unif Kernel |
| Asymmetric Smoothing Kernel | AKer.cos AKer.epa AKer.norm AKer.quar AKer.tri AKer.unif Kernel.asymmetric |
| Integrate Smoothing Kernels. | IKer.cos IKer.epa IKer.norm IKer.quar IKer.tri IKer.unif Kernel.integrate |
| K-Means Clustering for functional data | kmeans.center.ini kmeans.fd |
| ldata class definition and utilities | c.ldata is.ldata ldata ldata.cen Math.ldata mean.fdata mean.ldata names.ldata NCOL.ldata ncol.ldata NROW.ldata nrow.ldata Ops.ldata plot.ldata subset.ldata Summary.ldata [.ldata |
| Impact points selection of functional predictor and regression using local maxima distance correlation (LMDC) | LMDC.regre LMDC.select |
| Mithochondiral calcium overload (MCO) data set | MCO |
| Distance Matrix Computation | metric.dist |
| DTW: Dynamic time warping | metric.DTW metric.TWED metric.WDTW |
| Compute the Hausdorff distances between two curves. | metric.hausdorff |
| Kullback-Leibler distance | metric.kl |
| Distance Matrix Computation for ldata and mfdata class object | metric.ldata metric.mfdata |
| Approximates Lp-metric distances for functional data. | metric.lp |
| mfdata class definition and utilities | c.mfdata is.mfdata Math.mfdata mean.mfdata mfdata mfdata.cen names.mfdata NCOL.mfdata ncol.mfdata NROW.mfdata nrow.mfdata Ops.mfdata plot.mfdata subset.mfdata Summary.mfdata [.mfdata |
| A wrapper for the na.omit and na.fail function for fdata object | na.fail.fdata na.omit.fdata |
| Approximates Lp-norm for functional data. | norm.fd norm.fdata |
| ops.fda.usc Options Settings | ops.fda.usc |
| Select the number of basis using GCV method. | min.basis optim.basis |
| Smoothing of functional data using nonparametric kernel estimation | min.np optim.np |
| outliers for functional dataset | outliers.depth.pond outliers.depth.trim Outliers.fdata outliers.lrt outliers.thres.lrt quantile.outliers.pond quantile.outliers.trim |
| Penalty matrix for higher order differences | P.penalty |
| PCvM statistic for the Functional Linear Model with scalar response | Adot PCvM.statistic |
| phoneme data | phoneme |
| Plot functional data: fdata class object | lines.fdata plot.bifd plot.depth plot.fdata plot.lfdata plot.mdepth title.fdata |
| poblenou data | poblenou |
| Predicts from a fitted classif object. | predict.classif |
| Predicts from a fitted classif.DD object. | predict.classif.DD |
| Predict method for functional linear model (fregre.fd class) | predict.fregre.fd |
| Predict method for functional response model | predict.fregre.fr |
| Predict method for functional linear model | predict.fregre.gkam predict.fregre.glm predict.fregre.gsam predict.fregre.lm predict.fregre.plm |
| Predictions from a functional gls object | predict.fregre.gls predict.fregre.igls |
| Ornstein-Uhlenbeck process | r.ou |
| Utils for generate functional data | gridfdata rcombfdata |
| Data-driven sampling of random directions guided by sample of functional data | rdir.pc |
| Statistics for testing the functional linear model using random projections | rp.flm.statistic |
| Goodness-of fit test for the functional linear model using random projections | rp.flm.test |
| Simulate several random processes. | rproc2fdata |
| Wild bootstrap residuals | rwild |
| Smoothing matrix with roughness penalties by basis representation. | S.basis |
| Smoothing matrix by nonparametric methods | S.KNN S.LCR S.LLR S.LPR S.np S.NW |
| Proximities between functional data | semimetric.basis |
| Proximities between functional data (semi-metrics) | semimetric.deriv semimetric.fourier semimetric.hshift semimetric.mplsr semimetric.NPFDA semimetric.pca |
| Subsetting | subset.fdata |
| Summarizes information from kernel classification methods. | print.classif summary.classif |
| Correlation for functional data by Principal Component Analysis | summary.fdata.comp |
| Summarizes information from fregre.fd objects. | plot.summary.lm print.fregre.fd print.fregre.igls print.fregre.plm summary.fregre.fd summary.fregre.igls summary.fregre.lm |
| Summarizes information from fregre.gkam objects. | print.fregre.gkam summary.fregre.gkam |
| tecator data | tecator |
| Sampling Variance estimates | Var.e Var.y |
| Weighting tools | weights4class |
