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PACE package for Functional Data Analysis and Empirical Dynamics

Installation of the current development version

You can install the development version of the package in R using:

devtools::install_github("functionaldata/tPACE")

Installation of the latest CRAN release

You can install the package in R using:

install.packages("fdapace")

Load Package and Data

Once installed you can load the package with:

library(fdapace)

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Version

Install

install.packages('fdapace')

Monthly Downloads

6,722

Version

0.5.9

License

BSD_3_clause + file LICENSE

Issues

Pull Requests

Stars

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Repository

https://github.com/functionaldata/tPACE

Maintainer

Yidong Zhou

Last Published

August 16th, 2022

Functions in fdapace (0.5.9)

Check data format

CreateModeOfVarPlot

Functional Principal Component Analysis: Mode of variation plot

Minimum bandwidth based on kNN criterion.

Create an orthogonal basis of K functions in [0, 1], with nGrid points.

Create the fitted sample path plot based on the results from FPCA().

CreateOutliersPlot

Functional Principal Component or Functional Singular Value Decomposition Scores Plot using 'bagplot' or 'KDE' methodology

Functional Additive Models

Functional clustering and identifying substructures of longitudinal data

Functional Concurrent Regression using 2D smoothing

Dynamical Correlation

CreateScreePlot

Create the scree plot for the fitted eigenvalues

Calculation of functional correlation between two simultaneously observed processes.

Bootstrap test of Dynamic Correlation

CreateStringingPlot

Create plots for observed and stringed high dimensional data

Obtain the derivatives of eigenfunctions/ eigenfunctions of derivatives (note: these two are not the same)

Functional Linear Models

Covariance Surface

Functional Cross Covariance between longitudinal variable Y and scalar variable Z

Two dimensional local linear kernel smoother to target derivatives.

One dimensional local linear kernel smoother

Create cross-correlation matrix from auto- and cross-covariance matrix

Functional Linear Models New

Conditional Quantile estimation with functional covariates

Bootstrap pointwise confidence intervals for the mean function for densely observed data.

Functional Singular Value Decomposition

Create cross-correlation matrix from auto- and cross-covariance matrix

Functional Variance Process Analysis for dense functional data

MakeLNtoZscore02y

Z-score height for age 0 to 24 months based on WHO standards

GetNormalisedSample

Normalise sparse multivariate functional data

MakeHCtoZscore02y

Z-score head-circumference for age 0 to 24 months based on WHO standards

Functional Cross Covariance between longitudinal variable Y and longitudinal variable X

MakeGPFunctionalData

Create a Dense Functional Data sample for a Gaussian process

Format FPCA input

Normalise a curve to a particular area, by multiplication with a factor

Iterative Smooth Backfitting Algorithm

Functional Principal Component Analysis

Optimal Designs for Functional and Longitudinal Data for Trajectory Recovery or Scalar Response Prediction

Fitted functional data from FPCA object

Iterative Smooth Backfitting Algorithm

Fitted functional data for derivatives from the FPCAder object

MakeBWtoZscore02y

Z-score body-weight for age 0 to 24 months based on WHO standards

Selects number of functional principal components for given FPCA output and selection criteria

CreateDiagnosticsPlot

Functional Principal Component Analysis Diagnostics plot

Set the PCA option list

Predict FPC scores and curves for a new sample given an FPCA object

Sparsify densely observed functional data

Sieve estimation: B-spline based estimation procedure for time-varying additive models. The VCAM function can be used to perform function-to-scalar regression.

Two dimensional local linear kernel smoother.

Functional Additive Models with Multiple Predictor Processes

Create a sparse Functional Data sample for a Gaussian Process

Stringing for High-Dimensional data

Number of eggs laid daily from medflies

Print a WFDA object

Compactly display the structure of an FPCA object

fdapace: Principal Analysis by Conditional Expectation and Applications in Functional Data Analysis (revised version 16 August 2019)

Functional clustering and identifying substructures of longitudinal data using kCFC.

Time-Warping in Functional Data Analysis: Pairwise curve synchronization for functional data

Simulate a standard Wiener processes (Brownian motions)

Trapezoid Rule Numerical Integration

Cumulative Trapezoid Rule Numerical Integration

Print an FPCA object

Print an FSVD object

Functional Principal Component Analysis Bandwidth Diagnostics plot

Convert support of a mu/phi/cov etc. to and from obsGrid and workGrid

CreateFuncBoxPlot

Create functional boxplot using 'bagplot', 'KDE' or 'pointwise' methodology

Creates a correlation surface plot based on the results from FPCA() or FPCder().

CreateDesignPlot

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.

Check option format