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fda.usc (version 1.5.0)

Functional Data Analysis and Utilities for Statistical Computing

Description

Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.

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Version

Install

install.packages('fda.usc')

Monthly Downloads

2,504

Version

1.5.0

License

GPL-2

Maintainer

Manuel Oviedo de la Fuente

Last Published

February 1st, 2019

Functions in fda.usc (1.5.0)

Mithochondiral calcium overload (MCO) data set

PCvM statistic for the Functional Linear Model with scalar response

fda.usc-package

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

Classification Fitting Functional Generalized Linear Models

Classification Fitting Functional Generalized Additive Models

fda.usc.internal

fda.usc internal functions

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

Computes the derivative of functional data object.

fdata S3 Group Generic Functions

Functional Regression with scalar response using basis representation.

fregre.basis.cv

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

Converts fdata class object into fd class object

Fitting Functional Linear Models

gridfdata, rcombfdata

Utils for generate functional data

Depth for multivariate fdata

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

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

Depth for a multivariate dataset

Provides the depth measure for multivariate data

Functional regression with scalar response using non-parametric kernel estimation

Sampling Variance estimates

Descriptive measures for functional data.

Approximates Lp-metric distances for functional data.

False Discorvery Rate (FDR)

Kernel Classifier from Functional Data

metric.hausdorff

Compute the Hausdorff distances between two curves.

Classification Fitting Functional Recursive Partitioning and Regression Trees

Conditional quantile

Classifier from Functional Data

Classification Fitting Functional Generalized Kernel Additive Models

create.fdata.basis

Create Basis Set for Functional Data of fdata class

Distance Correlation Statistic and t-Test

Fitting Functional Generalized Kernel Additive Models.

Fitting Functional Generalized Linear Models

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

Semi-functional partially linear model with scalar response.

The deviance score .

Kullback--Leibler distance

predict.fregre.GAM

Predict method for functional regression model

Principal components for functional data

Ornstein-Uhlenbeck process

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

Smoothing of functional data using nonparametric kernel estimation

Select the number of basis using GCV method.

Wild bootstrap residuals

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

summary.fregre.fd

Summarizes information from fregre.fd objects.

semimetric.NPFDA

Proximities between functional data (semi-metrics)

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

rp.flm.statistic

Statistic for testing the FLM using random projections

summary.classif

Summarizes information from kernel classification methods.

Kernel.asymmetric

Asymmetric Smoothing Kernel

Symmetric Smoothing Kernels.

Partial least squares components for functional data.

Variable Selection using Functional Additive Models

Kernel.integrate

Integrate Smoothing Kernels.

Fit of Functional Generalized Least Squares Model Iteratively

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

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

anova.onefactor

One--way anova model for functional data

DD-Classifier Based on DD-plot

Functional Regression with scalar response using Principal Components Analysis.

Functional influence measures

Conditional Distribution Function

Quantile for influence measures

Conditional mode

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

The cross-validation (CV) score

Plot functional data: fdata.

Depth for univariate fdata

Computation of depth measures for functional data

fdata.bootstrap

Bootstrap samples of a functional statistic

Smoothing matrix with roughness penalties by basis representation.

Smoothing matrix by nonparametric methods.

Fit Functional Linear Model Using Generalized Least Squares

Functional ANOVA with Random Project.

Fitting Functional Generalized Spectral Additive Models

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

Functional Penalized PLS regression with scalar response

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

Inner products of Functional Data Objects o class (fdata)

ANOVA for heteroscedastic data

Simpson integration

Simulate several random processes.

Approximates Lp-norm for functional data.

A wrapper for the order function

Proximities between functional data

predict.functional.response

Predict method for functional response model

F-test for the Functional Linear Model with scalar response

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

summary.fregre.gkam

Summarizes information from fregre.gkam objects.

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

predict.fregre.gls

Predictions from a functional gls object

fregre.basis.fr

Functional Regression with functional response using basis representation.

fregre.bootstrap

Bootstrap regression

fregre.ppc,fregre.ppls

Functional Penalized PC (or PLS) regression with scalar response

Functional penalized PC (or PLS) regression with scalar response using selection of number of PC (or PLS) components

K-Means Clustering for functional data

Distance Matrix Computation

predict.classif.DD

Predicts from a fitted classif.DD object.

predict.classif

Predicts from a fitted classif object.

predict.fregre.fd

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

semimetric.basis

Proximities between functional data

A wrapper for the split and unlist function for fdata object

The generalized correlated cross-validation (GCCV) score.

The generalized cross-validation (GCV) score.

Detecting outliers for functional dataset

Penalty matrix for higher order differences