Package: fdapace 0.6.0

Yidong Zhou

fdapace: Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Authors:Yidong Zhou [cre, aut], Han Chen [aut], Su I Iao [aut], Poorbita Kundu [aut], Hang Zhou [aut], Satarupa Bhattacharjee [aut], Cody Carroll [aut], Yaqing Chen [aut], Xiongtao Dai [aut], Jianing Fan [aut], Alvaro Gajardo [aut], Pantelis Z. Hadjipantelis [aut], Kyunghee Han [aut], Hao Ji [aut], Changbo Zhu [aut], Paromita Dubey [ctb], Shu-Chin Lin [ctb], Hans-Georg Müller [cph, ths, aut], Jane-Ling Wang [cph, ths, aut]

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fdapace/json (API)
NEWS

# Install 'fdapace' in R:
install.packages('fdapace', repos = c('https://functionaldata.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/functionaldata/tpace/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • medfly25 - Number of eggs laid daily from medflies

On CRAN:

61 exports 31 stars 4.63 score 69 dependencies 24 dependents 4 mentions 431 scripts 1.7k downloads

Last updated 3 months agofrom:e778562f5f. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 01 2024
R-4.5-win-x86_64OKSep 01 2024
R-4.5-linux-x86_64OKSep 01 2024
R-4.4-win-x86_64OKSep 01 2024
R-4.4-mac-x86_64OKSep 01 2024
R-4.4-mac-aarch64OKSep 01 2024
R-4.3-win-x86_64OKSep 01 2024
R-4.3-mac-x86_64OKSep 01 2024
R-4.3-mac-aarch64OKSep 01 2024

Exports:BwNNCheckDataCheckOptionsConvertSupportCreateBasisCreateBWPlotCreateCovPlotCreateDesignPlotCreateDiagnosticsPlotCreateFuncBoxPlotCreateModeOfVarPlotCreateOutliersPlotCreatePathPlotCreateScreePlotCreateStringingPlotcumtrapzRcppDyn_testDynCorrFAMFCCorFClustFCRegFLMFLMCIFOptDesFPCAFPCAderFPCquantileFSVDFVPAGetCovSurfaceGetCrCorYXGetCrCorYZGetCrCovYXGetCrCovYZGetMeanCIGetMeanCurveGetNormalisedSampleGetNormalizedSamplekCFCLwls1DLwls2DLwls2DDerivMakeBWtoZscore02yMakeFPCAInputsMakeGPFunctionalDataMakeHCtoZscore02yMakeLNtoZscore02yMakeSparseGPMultiFAMNormCurvToAreaSBFittingSelectKSetOptionsSparsifyStringingtrapzRcppTVAMVCAMWFDAWiener

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercolorspacedata.tabledigestevaluatefansifarverfastmapfontawesomeforeignFormulafsggplot2gluegridExtragtablehighrHmischtmlTablehtmltoolshtmlwidgetsisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetnumDerivpillarpkgconfigpracmaR6rappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Completion of Functional Fragments

Rendered fromdynFPCA.Rmdusingknitr::rmarkdownon Sep 01 2024.

Last update: 2021-10-11
Started: 2021-10-10

Functional PCA in R

Rendered fromfdapaceVig.Rmdusingknitr::rmarkdownon Sep 01 2024.

Last update: 2022-08-15
Started: 2017-12-01

Readme and manuals

Help Manual

Help pageTopics
Minimum bandwidth based on kNN criterion.BwNN
Check data formatCheckData
Check option formatCheckOptions
Convert support of a mu/phi/cov etc. to and from obsGrid and workGridConvertSupport
Create an orthogonal basis of K functions in [0, 1], with nGrid points.CreateBasis
Functional Principal Component Analysis Bandwidth Diagnostics plotCreateBWPlot
Creates a correlation surface plot based on the results from FPCA() or FPCder().CreateCovPlot
Create design plots for functional data. See Yao, F., Müller, H.G., Wang, J.L. (2005). Functional data analysis for sparse longitudinal data. J. American Statistical Association 100, 577-590 for interpretation and usage of these plots. This function will open a new device as default.CreateDesignPlot
Functional Principal Component Analysis Diagnostics plotCreateDiagnosticsPlot plot.FPCA
Create functional boxplot using 'bagplot', 'KDE' or 'pointwise' methodologyCreateFuncBoxPlot
Functional Principal Component Analysis: Mode of variation plotCreateModeOfVarPlot
Functional Principal Component or Functional Singular Value Decomposition Scores Plot using 'bagplot' or 'KDE' methodologyCreateOutliersPlot
Create the fitted sample path plot based on the results from FPCA().CreatePathPlot
Create the scree plot for the fitted eigenvaluesCreateScreePlot
Create plots for observed and stringed high dimensional dataCreateStringingPlot
Cumulative Trapezoid Rule Numerical IntegrationcumtrapzRcpp
Bootstrap test of Dynamic CorrelationDyn_test
Dynamical CorrelationDynCorr
Functional Additive ModelsFAM
Calculation of functional correlation between two simultaneously observed processes.FCCor
Functional clustering and identifying substructures of longitudinal dataFClust
Functional Concurrent Regression using 2D smoothingFCReg
fdapace: Functional Data Analysis and Empirical Dynamicsfdapace
Fitted functional data from FPCA objectfitted.FPCA
Fitted functional data for derivatives from the FPCAder objectfitted.FPCAder
Functional Linear ModelsFLM
Confidence Intervals for Functional Linear Models.FLMCI
Optimal Designs for Functional and Longitudinal Data for Trajectory Recovery or Scalar Response PredictionFOptDes
Functional Principal Component AnalysisFPCA
Obtain the derivatives of eigenfunctions/ eigenfunctions of derivatives (note: these two are not the same)FPCAder
Conditional Quantile estimation with functional covariatesFPCquantile
Functional Singular Value DecompositionFSVD
Functional Variance Process Analysis for dense functional dataFVPA
Covariance SurfaceGetCovSurface
Create cross-correlation matrix from auto- and cross-covariance matrixGetCrCorYX
Create cross-correlation matrix from auto- and cross-covariance matrixGetCrCorYZ
Functional Cross Covariance between longitudinal variable Y and longitudinal variable XGetCrCovYX
Functional Cross Covariance between longitudinal variable Y and scalar variable ZGetCrCovYZ
Bootstrap pointwise confidence intervals for the mean function for densely observed data.GetMeanCI
Mean CurveGetMeanCurve
Normalise sparse multivariate functional dataGetNormalisedSample GetNormalizedSample
Functional clustering and identifying substructures of longitudinal data using kCFC.kCFC
One dimensional local linear kernel smootherLwls1D
Two dimensional local linear kernel smoother.Lwls2D
Two dimensional local linear kernel smoother to target derivatives.Lwls2DDeriv
Z-score body-weight for age 0 to 24 months based on WHO standardsMakeBWtoZscore02y
Format FPCA inputMakeFPCAInputs
Create a Dense Functional Data sample for a Gaussian processMakeGPFunctionalData
Z-score head-circumference for age 0 to 24 months based on WHO standardsMakeHCtoZscore02y
Z-score height for age 0 to 24 months based on WHO standardsMakeLNtoZscore02y
Create a sparse Functional Data sample for a Gaussian ProcessMakeSparseGP
Number of eggs laid daily from medfliesmedfly25
Functional Additive Models with Multiple Predictor ProcessesMultiFAM
Normalize a curve to a particular area, by multiplication with a factorNormCurvToArea
Predict FPC scores and curves for a new sample given an FPCA objectpredict.FPCA
Print an FPCA objectprint.FPCA
Print an FSVD objectprint.FSVD
Print a WFDA objectprint.WFDA
Iterative Smooth Backfitting AlgorithmSBFitting
Selects number of functional principal components for given FPCA output and selection criteriaSelectK
Set the PCA option listSetOptions
Sparsify densely observed functional dataSparsify
Compactly display the structure of an FPCA objectstr.FPCA
Stringing for High-Dimensional dataStringing
Trapezoid Rule Numerical IntegrationtrapzRcpp
Iterative Smooth Backfitting AlgorithmTVAM
Sieve estimation: B-spline based estimation procedure for time-varying additive models. The VCAM function can be used to perform function-to-scalar regression.VCAM
Time-Warping in Functional Data Analysis: Pairwise curve synchronization for functional dataWFDA
Simulate a standard Wiener processes (Brownian motions)Wiener