The cross-validation (CV) score
Symmetric Smoothing Kernels.
Asymmetric Smoothing Kernel
False Discorvery Rate (FDR)
Integrate Smoothing Kernels.
Descriptive measures for functional data.
The generalized correlated cross-validation (GCCV) score.
The generalized correlated cross-validation (GCCV) score
Impact points selection of functional predictor and regression using local
maxima distance correlation (LMDC)
Mithochondiral calcium overload (MCO) data set
Smoothing matrix by nonparametric methods
Sampling Variance estimates
Smoothing matrix with roughness penalties by basis representation.
PCvM statistic for the Functional Linear Model with scalar response
DD-Classifier Based on DD-plot
Functional classification using ML algotithms
Functional Classification usign k-fold CV
Kernel Classifier from Functional Data
Classification Fitting Functional Generalized Kernel Additive Models
aemet data
Performance measures for regression and classification models
Classifier from Functional Data
Penalty matrix for higher order differences
outliers for functional dataset
Create Basis Set for Functional Data of fdata class
Conditional quantile
Classification Fitting Functional Generalized Linear Models
ANOVA for heteroscedastic data
Classification Fitting Functional Generalized Additive Models
One--way anova model for functional data
The deviance score
Delsol, Ferraty and Vieu test for no functional-scalar interaction
Provides the depth measure for multivariate data
Proximities between functional data
Provides the depth measure for a list of p--functional data objects
Functional ANOVA with Random Project.
Conditional Distribution Function
Principal components for functional data
Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
Partial least squares components for functional data.
fda.usc internal functions
Functional data centred (subtract the mean of each discretization point)
Conditional mode
Functional Regression with functional response using basis representation.
Bootstrap regression
Fitting Functional Generalized Kernel Additive Models.
Fitting Functional Generalized Linear Models
Fitting Functional Linear Models
Functional regression with scalar response using non-parametric kernel
estimation
Computes the derivative of functional data object.
Calculation of the smoothing parameter (h) for a functional data
Functional influence measures
ldata class definition and utilities
Semi-functional partially linear model with scalar response.
Functional penalized PC regression with scalar response using selection of
number of PC components
fdata S3 Group Generic Functions
Variable Selection using Functional Additive Models
Converts fdata class object into fd class object
Fitting Functional Generalized Spectral Additive Models
Fit Functional Linear Model Using Generalized Least Squares
Fit of Functional Generalized Least Squares Model Iteratively
Functional Regression with scalar response using basis representation.
phoneme data
Plot functional data: fdata class object
Distance Matrix Computation
DTW: Dynamic time warping
Compute the Hausdorff distances between two curves.
Cross-validation Functional Regression with scalar response using basis
representation.
Kullback--Leibler distance
Cross-validation functional regression with scalar response using kernel
estimation.
Goodness-of fit test for the functional linear model using random
projections
Predict method for functional regression model
Statistics for testing the functional linear model using random projections
Ornstein-Uhlenbeck process
Inner products of Functional Data Objects o class (fdata)
Functional Regression with scalar response using Principal Components
Analysis
Quantile for influence measures
Weighting tools
Distance Correlation Statistic and t-Test
Distance Matrix Computation for ldata and mfdata class object
Computation of depth measures for functional data
Approximates Lp-norm for functional data.
Smoothing of functional data using nonparametric kernel estimation
Bootstrap samples of a functional statistic
Utils for generate functional data
F-test for the Functional Linear Model with scalar response
Converts raw data or other functional data classes into fdata class.
Select the number of basis using GCV method.
Data-driven sampling of random directions guided by sample of functional
data
Correlation for functional data by Principal Component Analysis
Predict method for functional linear model (fregre.fd class)
Goodness-of-fit test for the Functional Linear Model with scalar response
Summarizes information from fregre.fd objects.
Predicts from a fitted classif object.
Subsetting
Summarizes information from kernel classification methods.
tecator data
Summarizes information from fregre.gkam objects.
Functional Penalized PLS regression with scalar response
Simpson integration
Functional penalized PLS regression with scalar response using selection of
number of PLS components
K-Means Clustering for functional data
Predictions from a functional gls object
Wild bootstrap residuals
A wrapper for the na.omit and na.fail function for fdata object
Simulate several random processes.
Predict method for functional response model
ops.fda.usc Options Settings
Approximates Lp-metric distances for functional data.
Predicts from a fitted classif.DD object.
poblenou data
Proximities between functional data (semi-metrics)
Proximities between functional data