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fda.usc: Functional Data Analysis and Utilities for Statistical Computing

Package overview

fda.usc package carries out exploratory and descriptive analysis of functional data exploring its most important features such as depth measurements or functional outliers detection, among others. It also helps to explain and model the relationship between a dependent variable and independent (regression models) and make predictions. Methods for supervised or unsupervised classification of a set of functional data regarding a feature of the data are also included. It can perform functional ANOVA, hypothesis testing, functional response models and many others.

Installation

You can install the current fda.usc version from CRAN with:

install.packages("fda.usc")

or the latest patched version from Github with:

library(devtools)
devtools::install_github("moviedo5/fda.usc")

Issues & Feature Requests

For issues, bugs, feature requests etc. please use the Github Issues. Input is always welcome.

Documentation

A hands on introduction to can be found in the reference vignette.

Details on specific functions are in the reference manual.

Cheatsheet fda.usc reference card.

References

Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4):1-28. http://www.jstatsoft.org/v51/i04/

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Version

Install

install.packages('fda.usc')

Monthly Downloads

2,819

Version

2.0.1

License

GPL-2

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/moviedo5/fda.usc

Homepage

http://www.jstatsoft.org/v51/i04/

Maintainer

Manuel Oviedo de la Fuente

Last Published

December 16th, 2019

Functions in fda.usc (2.0.1)

The cross-validation (CV) score

Symmetric Smoothing Kernels.

Kernel.asymmetric

Asymmetric Smoothing Kernel

False Discorvery Rate (FDR)

Kernel.integrate

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

Performance measures for regression and classification models

Classifier from Functional Data

Penalty matrix for higher order differences

outliers for functional dataset

create.fdata.basis

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

fanova.onefactor

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

fda.usc-package

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

Partial least squares components for functional data.

fda.usc.internal

fda.usc internal functions

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

Conditional mode

fregre.basis.fr

Functional Regression with functional response using basis representation.

fregre.bootstrap

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

influence.fregre.fd

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.

Plot functional data: fdata class object

Distance Matrix Computation

DTW: Dynamic time warping

metric.hausdorff

Compute the Hausdorff distances between two curves.

fregre.basis.cv

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.fregre.gkam

Predict method for functional regression model

rp.flm.statistic

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

fdata.bootstrap

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

summary.fdata.comp

Correlation for functional data by Principal Component Analysis

predict.fregre.fd

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

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

summary.fregre.fd

Summarizes information from fregre.fd objects.

predict.classif

Predicts from a fitted classif object.

summary.classif

Summarizes information from kernel classification methods.

summary.fregre.gkam

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

kmeans.center.ini

K-Means Clustering for functional data

predict.fregre.gls

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.fregre.fr

Predict method for functional response model

ops.fda.usc Options Settings

Approximates Lp-metric distances for functional data.

predict.classif.DD

Predicts from a fitted classif.DD object.

semimetric.NPFDA

Proximities between functional data (semi-metrics)

semimetric.basis

Proximities between functional data