# Functional Data Analysis and Empirical Dynamics

## Documentation for package ‘fdapace’ version 0.4.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

fdapace-package | PACE: Principal Analysis by Conditional Expectation
BwNN | Minimum bandwidth based on kNN criterion.
CheckData | Check data format
CheckOptions | Check option format
ConvertSupport | Convert support of a mu/phi/cov etc. to and from obsGrid and workGrid
CreateBasis | Create an orthogonal basis of K functions in [0, 1], with nGrid points.
CreateBWPlot | Functional Principal Component Analysis Bandwidth Diagnostics plot
CreateCovPlot | Create the covariance surface plot based on the results from FPCA() or FPCder().
CreateDesignPlot | Create the design plot of the functional data.
CreateDiagnosticsPlot | Functional Principal Component Analysis Diagnostics plot
CreateFuncBoxPlot | Create functional boxplot using 'bagplot', 'KDE' or 'pointwise' methodology
CreateModeOfVarPlot | Functional Principal Component Analysis mode of variation plot
CreateOutliersPlot | Functional Principal Component or Functional Singular Value Decomposition Scores Plot using 'bagplot' or 'KDE' methodology
CreatePathPlot | Create the fitted sample path plot based on the results from FPCA().
CreateScreePlot | Create the scree plot for the fitted eigenvalues
CreateStringingPlot | Create plots for observed and stringed high dimensional data
cumtrapzRcpp | Cumulative Trapezoid Rule Numerical Integration
FAM | Functional Additive Models
FCCor | Calculate functional correlation between two simultaneously observed processes.
FClust | Functional clustering and identifying substructures of longitudinal data
FCReg | Functional Concurrent Regression by 2D smoothing method.
fdapace | PACE: Principal Analysis by Conditional Expectation
fitted.FPCA | Fitted functional sample from FPCA object
fitted.FPCAder | Fitted functional sample from FPCAder object
FOptDes | Optimal Designs for Functional and Longitudinal Data for Trajectory Recovery or Scalar Response Prediction
FPCA | Functional Principal Component Analysis
FPCAder | Take derivative of an FPCA object
FPCReg | Function for performing functonal linear regression where the covariates are functions X1(t1),X2(t2),.. and the response is a function Y(t_y).
FSVD | Functional Singular Value Decomposition
FVPA | Functional Variance Process Analysis for dense functional data
GetCrCorYX | Make cross-correlation matrix from auto- and cross-covariance matrix
GetCrCorYZ | Make cross-correlation matrix from auto- and cross-covariance matrix
GetCrCovYX | Functional Cross Covariance between longitudinal variable Y and longitudinal variable X
GetCrCovYZ | Functional Cross Covariance between longitudinal variable Y and scalar variable Z
GetNormalisedSample | Normalise sparse functional sample
GetNormalizedSample | Normalise sparse functional sample
kCFC | Functional clustering and identifying substructures of longitudinal data using kCFC.
Lwls1D | One dimensional local linear kernel smoother
Lwls2D | Two dimensional local linear kernel smoother.
Lwls2DDeriv | Two dimensional local linear kernel smoother with derivatives.
MakeBWtoZscore02y | Z-score body-weight for age 0 to 24 months based on WHO standards
MakeFPCAInputs | Format FPCA input
MakeGPFunctionalData | Make Gaussian Process Dense Functional Data sample
MakeHCtoZscore02y | Z-score head-circumference for age 0 to 24 months based on WHO standards
MakeLNtoZscore02y | Z-score height for age 0 to 24 months based on WHO standards
MakeSparseGP | Make Gaussian Process Sparse Functional Data sample
medfly25 | Number of eggs laid daily from medflies
MultiFAM | Functional Additive Models with Multiple Predictor Processes
NormCurvToArea | Normalise a curve to a particular area.
plot.FPCA | Functional Principal Component Analysis Diagnostics plot
predict.FPCA | Predict FPC scores for a new sample given an FPCA object
print.FPCA | Print an FPCA object
print.FSVD | Print an FSVD object
print.WFDA | Print a WFDA object
SBFitting | Iterative Smooth Backfitting Algorithm
SelectK | Selects number of functional principal components for given FPCA output and selection criteria
SetOptions | Set the PCA option list
Sparsify | Sparsify densely observed functional data
str.FPCA | Compactly display the structure of an FPCA object
Stringing | Stringing for High-Dimensional data
trapzRcpp | Trapezoid Rule Numerical Integration
WFDA | Warped Functional DAta Analysis
Wiener | Simulate standard Wiener processes (Brownian motions)