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.
Readme
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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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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Details
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 |
imports | doParallel , foreach , graphics , grDevices , iterators , methods , nlme , parallel , stats , utils |
depends | fda , MASS , mgcv , R (>= 2.10) , splines |
suggests | rmarkdown |
Contributors | Manuel Febrero Bande, Pedro Galeano, Alicia Nieto, Eduardo Garcia-Portugues |
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