A C D F G H I K L M N O P Q R S T V misc
fda.usc-package | Functional Data Analysis and Utilities for Statistical Computing (fda.usc) |
*.fdata | fda.usc internal functions |
+.fdata | fda.usc internal functions |
-.fdata | fda.usc internal functions |
/.fdata | fda.usc internal functions |
==.fdata | fda.usc internal functions |
aemet | aemet data |
AKer.cos | Asymmetric Smoothing Kernel |
AKer.epa | Asymmetric Smoothing Kernel |
AKer.norm | Asymmetric Smoothing Kernel |
AKer.quar | Asymmetric Smoothing Kernel |
AKer.tri | Asymmetric Smoothing Kernel |
AKer.unif | Asymmetric Smoothing Kernel |
anova.hetero | ANOVA for heteroscedastic data |
anova.RPm | Functional ANOVA with Random Project. |
anova.RPm.boot | Functional ANOVA with Random Project. |
c.fdata | fda.usc internal functions |
classif.kernel.fb | Kernel classifier from Functional Data Training by basis representation |
classif.kernel.fd | Kernel Classifier from Functional Data |
classif.knn.fd | k-Nearest Neighbor Classifier from Functional Data |
cond.F | Conditional Distribution Function |
cond.mode | Conditional mode |
cond.quantile | Conditional quantile |
count.na | fda.usc internal functions |
create.fdata.basis | Create Basis Set for Functional Data of fdata class |
create.pc.basis | Create Basis Set for Functional Data of fdata class |
CV.S | The cross-validation (CV) score |
Depth | Provides the depth measure for functional data |
depth.FM | Fraiman-Muniz depth measure |
depth.mode | Provides the depth measure (mode) for functional data |
depth.RP | Provides the depth measure using random projections for functional data |
depth.RPD | Provides the depth measure by random projections using derivatives |
Descriptive | Descriptive measures for functional data. |
dim.fdata | fda.usc internal functions |
fda.usc | Functional Data Analysis and Utilities for Statistical Computing (fda.usc) |
fdata | Converts raw data or other functional data classes into fdata class. |
fdata.bootstrap | Bootstrap samples of a functional statistic |
fdata.cen | Functional data centred (subtract the mean of each discretization point) |
fdata.deriv | Computes the derivative of functional data object. |
fdata2fd | Converts fdata class object into fd class object |
FDR | False Discorvery Rate (FDR) |
fregre.basis | Functional Regression with scalar response using basis representation. |
fregre.basis.cv | Cross-validation Functional Regression with scalar response using basis representation. |
fregre.combn | Functional Regression using selection of number of principal components |
fregre.glm | Fitting Functional Generalized Linear Models |
fregre.lm | Fitting Functional Linear Models |
fregre.np | Functional regression with scalar response using non-parametric kernel estimation |
fregre.np.cv | Cross-validation functional regression with scalar response using kernel estimation. |
fregre.pc | Functional Regression with scalar response using Principal Components Analysis. |
fregre.pc.cv | Functional Regression using selection of number of principal components |
fregre.plm | Semi-functional linear regression with scalar response. |
func.mean | Descriptive measures for functional data. |
func.med.FM | Descriptive measures for functional data. |
func.med.mode | Descriptive measures for functional data. |
func.med.RP | Descriptive measures for functional data. |
func.med.RPD | Descriptive measures for functional data. |
func.trim.FM | Descriptive measures for functional data. |
func.trim.mode | Descriptive measures for functional data. |
func.trim.RP | Descriptive measures for functional data. |
func.trim.RPD | Descriptive measures for functional data. |
func.trimvar.FM | Descriptive measures for functional data. |
func.trimvar.mode | Descriptive measures for functional data. |
func.trimvar.RP | Descriptive measures for functional data. |
func.trimvar.RPD | Descriptive measures for functional data. |
func.var | Descriptive measures for functional data. |
GCV.S | The generalized cross-validation (GCV) score. |
h.default | Calculation of the smoothing parameter (h) for a functional data |
hshift | Proximities between functional data (semi-metrics) |
IKer.cos | Integrate Smoothing Kernels. |
IKer.epa | Integrate Smoothing Kernels. |
IKer.norm | Integrate Smoothing Kernels. |
IKer.quar | Integrate Smoothing Kernels. |
IKer.tri | Integrate Smoothing Kernels. |
IKer.unif | Integrate Smoothing Kernels. |
influence.fdata | Functional influence measures |
influence.quan | Quantile for influence measures |
inprod.fdata | Inner products of Functional Data Objects o class (fdata) |
intercambio | Functional ANOVA with Random Project. |
intercambio.l | Functional ANOVA with Random Project. |
is.fdata | fda.usc internal functions |
Ker.cos | Symmetric Smoothing Kernels. |
Ker.epa | Symmetric Smoothing Kernels. |
Ker.norm | Symmetric Smoothing Kernels. |
Ker.quar | Symmetric Smoothing Kernels. |
Ker.tri | Symmetric Smoothing Kernels. |
Ker.unif | Symmetric Smoothing Kernels. |
Kernel | Symmetric Smoothing Kernels. |
Kernel.asymmetric | Asymmetric Smoothing Kernel |
Kernel.integrate | Integrate Smoothing Kernels. |
kmeans.assig.groups | K-Means Clustering for functional data |
kmeans.center.ini | K-Means Clustering for functional data |
kmeans.centers.update | K-Means Clustering for functional data |
kmeans.fd | K-Means Clustering for functional data |
lines.fdata | Plot functional data: fdata. |
metric.lp | Aproximates Lp-metric distances for functional data. |
min.basis | Select the number of basis using GCV method. |
min.np | Smoothing of functional data using nonparametric kernel estimation |
mplsr | Proximities between functional data (semi-metrics) |
ncol.fdata | fda.usc internal functions |
norm.fd | Aproximates Lp-norm for functional data. |
norm.fdata | Aproximates Lp-norm for functional data. |
nrow.fdata | fda.usc internal functions |
outliers.depth.pond | Detecting outliers for functional dataset |
outliers.depth.trim | Detecting outliers for functional dataset |
Outliers.fdata | Detecting outliers for functional dataset |
outliers.lrt | Detecting outliers for functional dataset |
outliers.thres.lrt | Detecting outliers for functional dataset |
pc.cor | Correlation for functional data by Principal Component Analysis |
pc.fdata | Principal components for functional data |
pc.svd.fdata | Principal components for functional data |
phoneme | phoneme data |
plot.fdata | Plot functional data: fdata. |
poblenou | poblenou data |
predict.classif.fd | Predicts from a fitted classif.fd object. |
predict.fregre.fd | Predict method for functional linear model (fregre.fd class) |
predict.fregre.glm | Predict method for functional linear model of fregre.glm fits object |
predict.fregre.lm | Predict method for functional linear model of fregre.lm fits object |
predict.fregre.plm | Predict method for semi-functional linear regression model. |
print.classif.fd | Summarizes information from kernel classification methods. |
print.fregre.fd | Summarizes information from fregre.fd objects. |
pvalue.FDR | False Discorvery Rate (FDR) |
quantile.outliers.pond | Detecting outliers for functional dataset |
quantile.outliers.trim | Detecting outliers for functional dataset |
rkernel | Symmetric Smoothing Kernels. |
S.basis | Smoothing matrix with roughness penalties by basis representation. |
S.KNN | Smoothing matrix by nonparametric methods. |
S.LLR | Smoothing matrix by nonparametric methods. |
S.np | Smoothing matrix by nonparametric methods. |
S.NW | Smoothing matrix by nonparametric methods. |
semimetric.basis | Proximities between functional data |
semimetric.deriv | Proximities between functional data (semi-metrics) |
semimetric.fourier | Proximities between functional data (semi-metrics) |
semimetric.hshift | Proximities between functional data (semi-metrics) |
semimetric.mplsr | Proximities between functional data (semi-metrics) |
semimetric.NPFDA | Proximities between functional data (semi-metrics) |
semimetric.pca | Proximities between functional data (semi-metrics) |
summary.anova | Functional ANOVA with Random Project. |
summary.classif.fd | Summarizes information from kernel classification methods. |
summary.fregre.fd | Summarizes information from fregre.fd objects. |
tecator | tecator data |
title.fdata | Plot functional data: fdata. |
traza | fda.usc internal functions |
Var.e | Sampling Variance estimates |
Var.y | Sampling Variance estimates |
[.fdata | fda.usc internal functions |