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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

2,583

Version

0.6.0

License

BSD_3_clause + file LICENSE

Issues

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Repository

https://github.com/functionaldata/tPACE

Maintainer

Last Published

July 3rd, 2024

Functions in fdapace (0.6.0)

Calculation of functional correlation between two simultaneously observed processes.

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

Functional Linear Models

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

Conditional Quantile estimation with functional covariates

Functional Singular Value Decomposition

Functional Variance Process Analysis for dense functional data

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

Functional Principal Component Analysis

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

Covariance Surface

Confidence Intervals for Functional Linear Models.

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

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

Functional Cross Covariance between longitudinal variable Y and scalar variable Z

Two dimensional local linear kernel smoother.

GetNormalisedSample

Normalise sparse multivariate functional data

One dimensional local linear kernel smoother

Functional Cross Covariance between longitudinal variable Y and longitudinal variable X

Two dimensional local linear kernel smoother to target derivatives.

MakeBWtoZscore02y

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

Create a sparse Functional Data sample for a Gaussian Process

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

Iterative Smooth Backfitting Algorithm

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

Format FPCA input

MakeHCtoZscore02y

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

MakeGPFunctionalData

Create a Dense Functional Data sample for a Gaussian process

MakeLNtoZscore02y

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

Set the PCA option list

Functional Additive Models with Multiple Predictor Processes

Cumulative Trapezoid Rule Numerical Integration

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

Simulate a standard Wiener processes (Brownian motions)

Fitted functional data for derivatives from the FPCAder object

Iterative Smooth Backfitting Algorithm

Stringing for High-Dimensional data

fdapace: Functional Data Analysis and Empirical Dynamics

Sparsify densely observed functional data

Fitted functional data from FPCA object

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

CreateDiagnosticsPlot

Functional Principal Component Analysis Diagnostics plot

Print a WFDA object

Functional clustering and identifying substructures of longitudinal data using kCFC.

Trapezoid Rule Numerical Integration

Print an FPCA object

Number of eggs laid daily from medflies

Compactly display the structure of an FPCA object

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

Print an FSVD object

Minimum bandwidth based on kNN criterion.

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

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

Functional Principal Component Analysis Bandwidth Diagnostics plot

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

CreateModeOfVarPlot

Functional Principal Component Analysis: Mode of variation plot

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

Check data format

CreateFuncBoxPlot

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

CreateScreePlot

Create the scree plot for the fitted eigenvalues

CreateStringingPlot

Create plots for observed and stringed high dimensional data

Functional Concurrent Regression using 2D smoothing

Functional clustering and identifying substructures of longitudinal data

Bootstrap test of Dynamic Correlation

Functional Additive Models

Dynamical Correlation

CreateOutliersPlot

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