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