| accuracy | Performance measures for regression and classification models |
| aemet | aemet data |
| classif.DD | DD-Classifier Based on DD-plot |
| classif.depth | Classifier from Functional Data |
| classif.gkam | Classification Fitting Functional Generalized Kernel Additive... |
| classif.glm | Classification Fitting Functional Generalized Linear Models |
| classif.gsam | Classification Fitting Functional Generalized Additive Models |
| classif.gsam.vs | Variable Selection in Functional Data Classification |
| classif.kfold | Functional Classification usign k-fold CV |
| classif.ML | Functional classification using ML algotithms |
| classif.np | Kernel Classifier from Functional Data |
| cond.F | Conditional Distribution Function |
| cond.mode | Conditional mode |
| cond.quantile | Conditional quantile |
| create.fdata.basis | Create Basis Set for Functional Data of fdata class |
| CV.S | The cross-validation (CV) score |
| dcor.xy | Distance Correlation Statistic and t-Test |
| depth.fdata | Computation of depth measures for functional data |
| depth.mdata | Provides the depth measure for multivariate data |
| depth.mfdata | Provides the depth measure for a list of p-functional data... |
| Descriptive | Descriptive measures for functional data. |
| dev.S | The deviance score |
| dfv.test | Delsol, Ferraty and Vieu test for no functional-scalar... |
| dis.cos.cor | Proximities between functional data |
| fanova.hetero | ANOVA for heteroscedastic data |
| fanova.onefactor | One-way anova model for functional data |
| fanova.RPm | Functional ANOVA with Random Project. |
| fdata | Converts raw data or other functional data classes into fdata... |
| fdata2basis | Compute fucntional coefficients from functional data... |
| fdata2fd | Converts fdata class object into fd class object |
| fdata2pc | Principal components for functional data |
| fdata2pls | Partial least squares components for functional data. |
| fdata.bootstrap | Bootstrap samples of a functional statistic |
| fdata.cen | Functional data centred (subtract the mean of each... |
| fdata.deriv | Computes the derivative of functional data object. |
| fdata.methods | fdata S3 Group Generic Functions |
| fda.usc.internal | fda.usc internal functions |
| fda.usc-package | Functional Data Analysis and Utilities for Statistical... |
| FDR | False Discorvery Rate (FDR) |
| fEqDistrib.test | Tests for checking the equality of distributions between two... |
| fEqMoments.test | Tests for checking the equality of means and/or covariance... |
| flm.Ftest | F-test for the Functional Linear Model with scalar response |
| flm.test | Goodness-of-fit test for the Functional Linear Model with... |
| fregre.basis | Functional Regression with scalar response using basis... |
| fregre.basis.cv | Cross-validation Functional Regression with scalar response... |
| fregre.basis.fr | Functional Regression with functional response using basis... |
| fregre.bootstrap | Bootstrap regression |
| fregre.gkam | Fitting Functional Generalized Kernel Additive Models. |
| fregre.glm | Fitting Functional Generalized Linear Models |
| fregre.glm.vs | Variable Selection using Functional Linear Models |
| fregre.gls | Fit Functional Linear Model Using Generalized Least Squares |
| fregre.gsam | Fitting Functional Generalized Spectral Additive Models |
| fregre.gsam.vs | Variable Selection using Functional Additive Models |
| fregre.igls | Fit of Functional Generalized Least Squares Model Iteratively |
| fregre.lm | Fitting Functional Linear Models |
| fregre.np | Functional regression with scalar response using... |
| fregre.np.cv | Cross-validation functional regression with scalar response... |
| fregre.pc | Functional Regression with scalar response using Principal... |
| fregre.pc.cv | Functional penalized PC regression with scalar response using... |
| fregre.plm | Semi-functional partially linear model with scalar response. |
| fregre.pls | Functional Penalized PLS regression with scalar response |
| fregre.pls.cv | Functional penalized PLS regression with scalar response... |
| GCCV.S | The generalized correlated cross-validation (GCCV) score. |
| GCV.S | The generalized correlated cross-validation (GCCV) score |
| h.default | Calculation of the smoothing parameter (h) for a functional... |
| influence.fregre.fd | Functional influence measures |
| influence_quan | Quantile for influence measures |
| inprod.fdata | Inner products of Functional Data Objects o class (fdata) |
| int.simpson | Simpson integration |
| Kernel | Symmetric Smoothing Kernels. |
| Kernel.asymmetric | Asymmetric Smoothing Kernel |
| Kernel.integrate | Integrate Smoothing Kernels. |
| kmeans.fd | K-Means Clustering for functional data |
| ldata | ldata class definition and utilities |
| LMDC.select | Impact points selection of functional predictor and... |
| MCO | Mithochondiral calcium overload (MCO) data set |
| metric.dist | Distance Matrix Computation |
| metric.DTW | DTW: Dynamic time warping |
| metric.hausdorff | Compute the Hausdorff distances between two curves. |
| metric.kl | Kullback-Leibler distance |
| metric.ldata | Distance Matrix Computation for ldata and mfdata class object |
| metric.lp | Approximates Lp-metric distances for functional data. |
| mfdata | mfdata class definition and utilities |
| na.omit.fdata | A wrapper for the na.omit and na.fail function for fdata... |
| norm.fdata | Approximates Lp-norm for functional data. |
| ops.fda.usc | ops.fda.usc Options Settings |
| optim.basis | Select the number of basis using GCV method. |
| optim.np | Smoothing of functional data using nonparametric kernel... |
| Outliers.fdata | outliers for functional dataset |
| PCvM.statistic | PCvM statistic for the Functional Linear Model with scalar... |
| phoneme | phoneme data |
| plot.fdata | Plot functional data: fdata class object |
| poblenou | poblenou data |
| P.penalty | Penalty matrix for higher order differences |
| predict.classif | Predicts from a fitted classif object. |
| predict.classif.DD | Predicts from a fitted classif.DD object. |
| predict.fregre.fd | Predict method for functional linear model (fregre.fd class) |
| predict.fregre.fr | Predict method for functional response model |
| predict.fregre.gls | Predictions from a functional gls object |
| predict.fregre.lm | Predict method for functional linear model |
| rcombfdata | Utils for generate functional data |
| rdir.pc | Data-driven sampling of random directions guided by sample of... |
| r.ou | Ornstein-Uhlenbeck process |
| rp.flm.statistic | Statistics for testing the functional linear model using... |
| rp.flm.test | Goodness-of fit test for the functional linear model using... |
| rproc2fdata | Simulate several random processes. |
| rwild | Wild bootstrap residuals |
| S.basis | Smoothing matrix with roughness penalties by basis... |
| semimetric.basis | Proximities between functional data |
| semimetric.NPFDA | Proximities between functional data (semi-metrics) |
| S.np | Smoothing matrix by nonparametric methods |
| subset.fdata | Subsetting |
| summary.classif | Summarizes information from kernel classification methods. |
| summary.fdata.comp | Correlation for functional data by Principal Component... |
| summary.fregre.fd | Summarizes information from fregre.fd objects. |
| summary.fregre.gkam | Summarizes information from fregre.gkam objects. |
| tecator | tecator data |
| Var.y | Sampling Variance estimates |
| weights4class | Weighting tools |
