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fda.usc (version 0.9.4)
Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
Description
This package implements functional data methods.
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Install
install.packages('fda.usc')
Monthly Downloads
5,150
Version
0.9.4
License
GPL-2
Maintainer
Oviedo la Fuente
Last Published
March 9th, 2011
Functions in fda.usc (0.9.4)
Search all functions
phoneme
phoneme data
Depth
Provides the depth measure for functional data
Kernel.asymmetric
Asymmetric Smoothing Kernel
depth.mode
Provides the depth measure (mode) for functional data
S.basis
Smoothing matrix with roughness penalties by basis representation.
CV.S
The cross-validation (CV) score
Outliers.fdata
Detecting outliers for functional dataset
depth.FM
Fraiman-Muniz depth measure
fregre.glm
Fitting Functional Generalized Linear Models
S.np
Smoothing matrix by nonparametric methods.
fregre.pc.cv
Functional Regression using selection of number of principal components
min.np
Smoothing of functional data using nonparametric kernel estimation
fdata
Converts raw data or other functional data classes into fdata class.
classif.knn.fd
k-Nearest Neighbor Classifier from Functional Data
anova.RPm
Functional ANOVA with Random Project.
create.fdata.basis
Create Basis Set for Functional Data of fdata class
fdata2fd
Converts fdata class object into fd class object
fdata.cen
Functional data centred (subtract the mean of each discretization point)
fregre.np.cv
Cross-validation functional regression with scalar response using kernel estimation.
fregre.lm
Fitting Functional Linear Models
influence.quan
Quantile for influence measures
fregre.np
Functional regression with scalar response using non-parametric kernel estimation
fdata.deriv
Computes the derivative of functional data object.
GCV.S
The generalized cross-validation (GCV) score.
Var.y
Sampling Variance estimates
fregre.pc
Functional Regression with scalar response using Principal Components Analysis.
fda.usc.internal
fda.usc internal functions
semimetric.NPFDA
Proximities between functional data (semi-metrics)
predict.fregre.plm
Predict method for semi-functional linear regression model.
summary.classif.fd
Summarizes information from kernel classification methods.
influnce.fdata
Functional influence measures
depth.RP
Provides the depth measure using random projections for functional data
predict.fregre.lm
Predict method for functional linear model of fregre.lm fits object
fregre.plm
Semi-functional linear regression with scalar response.
h.default
Calculation of the smoothing parameter (h) for a functional data
depth.RPD
Provides the depth measure by random projections using derivatives
fregre.basis
Functional Regression with scalar response using basis representation.
inprod.fdata
Inner products of Functional Data Objects o class (fdata)
cond.quantile
Conditional quantile
fdata.bootstrap
Bootstrap samples of a functional statistic
cond.F
Conditional Distribution Function
fregre.basis.cv
Cross-validation Functional Regression with scalar response using basis representation.
Kernel.integrate
Integrate Smoothing Kernels.
Descriptive
Descriptive measures for functional data.
cond.mode
Conditional mode
predict.fregre.glm
Predict method for functional linear model of fregre.glm fits object
fda.usc-package
Functional Data Analysis and Utilities for Statistical Computing (fda.usc)
summary.fregre.fd
Summarizes information from fregre.fd objects.
classif.kernel.fb
Kernel classifier from Functional Data Training by basis representation
aemet
aemet data
norm.fdata
Aproximates Lp-norm for functional data.
metric.lp
Aproximates Lp-metric distances for functional data.
poblenou
poblenou data
semimetric.basis
Proximities between functional data
pc.cor
Correlation for functional data by Principal Component Analysis
Kernel
Symmetric Smoothing Kernels.
kmeans.fd
K-Means Clustering for functional data
pc.fdata
Principal components for functional data
min.basis
Select the number of basis using GCV method.
tecator
tecator data
predict.classif.fd
Predicts from a fitted classif.fd object.
classif.kernel.fd
Kernel Classifier from Functional Data
FDR
False Discorvery Rate (FDR)
plot.fdata
Plot functional data: fdata.
predict.fregre.fd
Predict method for functional linear model (fregre.fd class)
anova.hetero
ANOVA for heteroscedastic data
pls.fdata
Partial least squares components for functional data.
fregre.pls.cv
Functional PLS regression with scalar response using selection of number of PLS components
int.simpson
Simpson integration
fdata2pls
Partial least squares components for functional data.
classif.glm
Classification Fitting Functional Generalized Linear Models
fregre.pls
Functional PLS regression with scalar response
classif.knn
k-Nearest Neighbor Classifier from Functional Data
dis.cos.cor
Proximities between functional data
fdata.methods
fdata S3 Group Generic Functions
predict.classif
Predicts from a fitted classif object.
fregre.kgam
Fitting Functional Generalized Additive Models.
classif.gsam
Classification Fitting Functional Generalized Additive Models
summary.classif
Summarizes information from kernel classification methods.
summary.fdata.comp
Correlation for functional data by Principal Component Analysis
fregre.bootstrap
Bootstrap regression
rproc2fdata
Generate random process of fdata class.
dev.S
The deviance score .
fdata2pc
Principal components for functional data
flm.test
Goodness-of-fit test for the Functional Linear Model with scalar response
fregre.gsam
Fitting Functional Generalized Spectral Additive Models
predict.fregre.kgam
Predict method for functional generalized additive model of fregre.kgam fits object
classif.kernel
Kernel Classifier from Functional Data
PCvM.statistic
PCvM statistic for the Functional Linear Model with scalar response
predict.fregre.gkam
Predict method for functional generalized kernel additive model of fregre.gkam fits object
classif.np
Kernel Classifier from Functional Data
fregre.gkam
Fitting Functional Generalized Kernel Additive Models.
predict.fregre.gsam
Predict method for functional generalized spectral additive model of fregre.gsam fits object
flm.Ftest
F-test for the Functional Linear Model with scalar response
classif.kgam
Classification Fitting Functional Generalized Additive Models
classif.gkam
Classification Fitting Functional Generalized Kernel Additive Models
rber.gold
Gold section bootstrap sampling
summary.fregre.kgam
Summarizes information from fregre.kgam objects.
dfv.test
Delsol, Ferraty and Vieu test for no functional-scalar interaction
summary.fregre.gkam
Summarizes information from fregre.gkam objects.