fda.usc v1.5.0

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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.

Functions in fda.usc

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

Type Package
Date 2019-02-01
License GPL-2
URL http://www.jstatsoft.org/v51/i04/
BugReports https://github.com/cran/fda.usc
LazyLoad yes
NeedsCompilation yes
Repository CRAN
Encoding UTF-8
RoxygenNote 6.1.1
Packaged 2019-02-01 10:42:04 UTC; moviedo
Date/Publication 2019-02-01 13:53:18 UTC

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