fda.usc v2.0.2


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Functional Data Analysis and Utilities for Statistical Computing

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.


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

CRAN\_Status\_Badge Licence

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.


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


or the latest patched version from Github with:


Issues & Feature Requests

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


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.


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/


Functions in fda.usc

Name Description
CV.S The cross-validation (CV) score
Kernel.integrate Integrate Smoothing Kernels.
Kernel.asymmetric Asymmetric Smoothing Kernel
Var.y Sampling Variance estimates
S.np Smoothing matrix by nonparametric methods
Descriptive Descriptive measures for functional data.
dis.cos.cor Proximities between functional data
fanova.RPm Functional ANOVA with Random Project.
fdata2fd Converts fdata class object into fd class object
LMDC.select Impact points selection of functional predictor and regression using local maxima distance correlation (LMDC)
classif.np Kernel Classifier from Functional Data
classif.kfold Functional Classification usign k-fold CV
fdata.methods fdata S3 Group Generic Functions
accuracy Performance measures for regression and classification models
GCV.S The generalized correlated cross-validation (GCCV) score
Kernel Symmetric Smoothing Kernels.
classif.DD DD-Classifier Based on DD-plot
MCO Mithochondiral calcium overload (MCO) data set
FDR False Discorvery Rate (FDR)
PCvM.statistic PCvM statistic for the Functional Linear Model with scalar response
classif.depth Classifier from Functional Data
fregre.glm Fitting Functional Generalized Linear Models
S.basis Smoothing matrix with roughness penalties by basis representation.
fregre.gkam Fitting Functional Generalized Kernel Additive Models.
classif.ML Functional classification using ML algotithms
cond.F Conditional Distribution Function
classif.gkam Classification Fitting Functional Generalized Kernel Additive Models
cond.quantile Conditional quantile
aemet aemet data
cond.mode Conditional mode
create.fdata.basis Create Basis Set for Functional Data of fdata class
dcor.xy Distance Correlation Statistic and t-Test
fda.usc-package Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
depth.fdata Computation of depth measures for functional data
dev.S The deviance score
depth.mdata Provides the depth measure for multivariate data
fregre.pls Functional Penalized PLS regression with scalar response
dfv.test Delsol, Ferraty and Vieu test for no functional-scalar interaction
depth.mfdata Provides the depth measure for a list of p--functional data objects
fdata2pls Partial least squares components for functional data.
GCCV.S The generalized correlated cross-validation (GCCV) score.
fdata2pc Principal components for functional data
fda.usc.internal fda.usc internal functions
fregre.lm Fitting Functional Linear Models
fregre.basis Functional Regression with scalar response using basis representation.
fregre.basis.fr Functional Regression with functional response using basis representation.
fregre.bootstrap Bootstrap regression
Outliers.fdata outliers for functional dataset
fregre.basis.cv Cross-validation Functional Regression with scalar response using basis representation.
fregre.np Functional regression with scalar response using non-parametric kernel estimation
metric.dist Distance Matrix Computation
h.default Calculation of the smoothing parameter (h) for a functional data
phoneme phoneme data
influence.fregre.fd Functional influence measures
fdata.cen Functional data centred (subtract the mean of each discretization point)
fregre.pls.cv Functional penalized PLS regression with scalar response using selection of number of PLS components
P.penalty Penalty matrix for higher order differences
plot.fdata Plot functional data: fdata class object
metric.hausdorff Compute the Hausdorff distances between two curves.
fregre.pc.cv Functional penalized PC regression with scalar response using selection of number of PC components
ldata ldata class definition and utilities
fregre.plm Semi-functional partially linear model with scalar response.
fregre.gls Fit Functional Linear Model Using Generalized Least Squares
fdata.deriv Computes the derivative of functional data object.
fregre.gsam Fitting Functional Generalized Spectral Additive Models
fregre.igls Fit of Functional Generalized Least Squares Model Iteratively
fregre.gsam.vs Variable Selection using Functional Additive Models
rdir.pc Data-driven sampling of random directions guided by sample of functional data
poblenou poblenou data
metric.lp Approximates Lp-metric distances for functional data.
predict.classif.DD Predicts from a fitted classif.DD object.
metric.DTW DTW: Dynamic time warping
predict.fregre.gkam Predict method for functional linear model
na.omit.fdata A wrapper for the na.omit and na.fail function for fdata object
classif.glm Classification Fitting Functional Generalized Linear Models
subset.fdata Subsetting
r.ou Ornstein-Uhlenbeck process
summary.fdata.comp Correlation for functional data by Principal Component Analysis
metric.kl Kullback--Leibler distance
rp.flm.test Goodness-of fit test for the functional linear model using random projections
classif.gsam Classification Fitting Functional Generalized Additive Models
rp.flm.statistic Statistics for testing the functional linear model using random projections
metric.ldata Distance Matrix Computation for ldata and mfdata class object
rcombfdata Utils for generate functional data
summary.classif Summarizes information from kernel classification methods.
summary.fregre.fd Summarizes information from fregre.fd objects.
fanova.hetero ANOVA for heteroscedastic data
fanova.onefactor One--way anova model for functional data
int.simpson Simpson integration
fdata.bootstrap Bootstrap samples of a functional statistic
ops.fda.usc ops.fda.usc Options Settings
flm.test Goodness-of-fit test for the Functional Linear Model with scalar response
kmeans.center.ini K-Means Clustering for functional data
predict.fregre.fr Predict method for functional response model
norm.fdata Approximates Lp-norm for functional data.
flm.Ftest F-test for the Functional Linear Model with scalar response
summary.fregre.gkam Summarizes information from fregre.gkam objects.
tecator tecator data
fdata Converts raw data or other functional data classes into fdata class.
semimetric.basis Proximities between functional data
fregre.np.cv Cross-validation functional regression with scalar response using kernel estimation.
semimetric.NPFDA Proximities between functional data (semi-metrics)
predict.fregre.gls Predictions from a functional gls object
fregre.pc Functional Regression with scalar response using Principal Components Analysis
inprod.fdata Inner products of Functional Data Objects o class (fdata)
influence_quan Quantile for influence measures
predict.fregre.fd Predict method for functional linear model (fregre.fd class)
optim.basis Select the number of basis using GCV method.
rwild Wild bootstrap residuals
optim.np Smoothing of functional data using nonparametric kernel estimation
weights4class Weighting tools
predict.classif Predicts from a fitted classif object.
rproc2fdata Simulate several random processes.
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Last month downloads


Type Package
Date 2020-02-17
License GPL-2
URL https://github.com/moviedo5/fda.usc, http://www.jstatsoft.org/v51/i04/
BugReports https://github.com/moviedo5/fda.usc
LazyLoad yes
NeedsCompilation yes
Repository CRAN
Encoding UTF-8
RoxygenNote 7.0.2
Packaged 2020-02-17 17:32:08 UTC; moviedo
Date/Publication 2020-02-17 19:00:34 UTC

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