fda.usc

The cross-validation (CV) score

Descriptive measures for functional data.

False Discorvery Rate (FDR)

The generalized correlated cross-validation (GCCV) score.

The generalized correlated cross-validation (GCCV) score

Symmetric Smoothing Kernels.

Asymmetric Smoothing Kernel

Integrate Smoothing Kernels.

Impact points selection of functional predictor and regression using local maxima distance correlation (LMDC)

Mithochondiral calcium overload (MCO) data set

outliers for functional dataset

Penalty matrix for higher order differences

PCvM statistic for the Functional Linear Model with scalar response

Smoothing matrix with roughness penalties by basis representation.

Smoothing matrix by nonparametric methods

Sampling Variance estimates

Performance measures for regression and classification models

aemet data

DD-Classifier Based on DD-plot

Functional classification using ML algotithms

Classifier from Functional Data

Classification Fitting Functional Generalized Kernel Additive Models

Classification Fitting Functional Generalized Linear Models

Classification Fitting Functional Generalized Additive Models

Functional Classification usign k-fold CV

Kernel Classifier from Functional Data

Conditional Distribution Function

Conditional mode

Conditional quantile

Create Basis Set for Functional Data of fdata class

Distance Correlation Statistic and t-Test

Computation of depth measures for functional data

Provides the depth measure for multivariate data

Provides the depth measure for a list of p–functional data objects

The deviance score

Delsol, Ferraty and Vieu test for no functional-scalar interaction

Proximities between functional data

Functional ANOVA with Random Project.

ANOVA for heteroscedastic data

One–way anova model for functional data

Functional Data Analysis and Utilities for Statistical Computing (fda.usc)

fda.usc internal functions

Converts raw data or other functional data classes into fdata class.

Bootstrap samples of a functional statistic

Functional data centred (subtract the mean of each discretization point)

Computes the derivative of functional data object.

fdata S3 Group Generic Functions

Converts fdata class object into fd class object

Principal components for functional data

Partial least squares components for functional data.

F-test for the Functional Linear Model with scalar response

Goodness-of-fit test for the Functional Linear Model with scalar response

Functional Regression with scalar response using basis representation.

Cross-validation Functional Regression with scalar response using basis representation.

Functional Regression with functional response using basis representation.

Bootstrap regression

Fitting Functional Generalized Kernel Additive Models.

Fitting Functional Generalized Linear Models

Fit Functional Linear Model Using Generalized Least Squares

Fitting Functional Generalized Spectral Additive Models

Variable Selection using Functional Additive Models

Fit of Functional Generalized Least Squares Model Iteratively

Fitting Functional Linear Models

Functional regression with scalar response using non-parametric kernel estimation

Cross-validation functional regression with scalar response using kernel estimation.

Functional Regression with scalar response using Principal Components Analysis

Functional penalized PC regression with scalar response using selection of number of PC components

Semi-functional partially linear model with scalar response.

Functional Penalized PLS regression with scalar response

Functional penalized PLS regression with scalar response using selection of number of PLS components

Calculation of the smoothing parameter (h) for a functional data

Functional influence measures

Quantile for influence measures

Inner products of Functional Data Objects o class (fdata)

Simpson integration

K-Means Clustering for functional data

ldata class definition and utilities

DTW: Dynamic time warping

Distance Matrix Computation

Compute the Hausdorff distances between two curves.

Kullback–Leibler distance

Distance Matrix Computation for ldata and mfdata class object

Approximates Lp-metric distances for functional data.

A wrapper for the na.omit and na.fail function for fdata object

Approximates Lp-norm for functional data.

ops.fda.usc Options Settings

Select the number of basis using GCV method.

Smoothing of functional data using nonparametric kernel estimation

phoneme data

Plot functional data: fdata class object

poblenou data

Predicts from a fitted classif object.

Predicts from a fitted classif.DD object.

Predict method for functional linear model (fregre.fd class)

Predict method for functional response model

Predictions from a functional gls object

Predict method for functional linear model

Ornstein-Uhlenbeck process

Utils for generate functional data

Data-driven sampling of random directions guided by sample of functional data

Statistics for testing the functional linear model using random projections

Goodness-of fit test for the functional linear model using random projections

Simulate several random processes.

Wild bootstrap residuals

Proximities between functional data (semi-metrics)

Proximities between functional data

Subsetting

Summarizes information from kernel classification methods.

Correlation for functional data by Principal Component Analysis

Summarizes information from fregre.fd objects.

Summarizes information from fregre.gkam objects.

tecator data

Weighting tools