Functional Data Analysis and Empirical Dynamics
Create cross-correlation matrix from auto- and cross-covariance matrix
Create cross-correlation matrix from auto- and cross-covariance matrix
Minimum bandwidth based on kNN criterion.
Check data format
Check option format
Convert support of a mu/phi/cov etc. to and from obsGrid and workGrid
Create an orthogonal basis of K functions in [0, 1], with nGrid points...
Functional Principal Component Analysis Bandwidth Diagnostics plot
Creates a correlation surface plot based on the results from FPCA() or...
Create design plots for functional data. See Yao, F., Müller, H.G., Wa...
fdapace: Functional Data Analysis and Empirical Dynamics
Create functional boxplot using 'bagplot', 'KDE' or 'pointwise' method...
Functional Principal Component Analysis: Mode of variation plot
Functional Principal Component or Functional Singular Value Decomposit...
Create the fitted sample path plot based on the results from FPCA().
Create the scree plot for the fitted eigenvalues
Fitted functional data from FPCA object
Create plots for observed and stringed high dimensional data
Cumulative Trapezoid Rule Numerical Integration
Bootstrap test of Dynamic Correlation
Dynamical Correlation
Functional Additive Models
Calculation of functional correlation between two simultaneously obser...
Functional clustering and identifying substructures of longitudinal da...
Functional Concurrent Regression using 2D smoothing
Fitted functional data for derivatives from the FPCAder object
Functional Linear Models
Confidence Intervals for Functional Linear Models.
Optimal Designs for Functional and Longitudinal Data for Trajectory Re...
Functional Principal Component Analysis
Obtain the derivatives of eigenfunctions/ eigenfunctions of derivative...
Conditional Quantile estimation with functional covariates
Functional Singular Value Decomposition
Functional Variance Process Analysis for dense functional data
Covariance Surface
Functional Cross Covariance between longitudinal variable Y and longit...
Functional Cross Covariance between longitudinal variable Y and scalar...
Bootstrap pointwise confidence intervals for the mean function for den...
Mean Curve
Format FPCA input
Normalise sparse multivariate functional data
Functional clustering and identifying substructures of longitudinal da...
One dimensional local linear kernel smoother
Two dimensional local linear kernel smoother.
Two dimensional local linear kernel smoother to target derivatives.
Z-score body-weight for age 0 to 24 months based on WHO standards
Create a Dense Functional Data sample for a Gaussian process
Z-score head-circumference for age 0 to 24 months based on WHO standar...
Z-score height for age 0 to 24 months based on WHO standards
Create a sparse Functional Data sample for a Gaussian Process
Functional Additive Models with Multiple Predictor Processes
Normalize a curve to a particular area, by multiplication with a facto...
Functional Principal Component Analysis Diagnostics plot
Predict FPC scores and curves for a new sample given an FPCA object
Print an FPCA object
Print an FSVD object
Print a WFDA object
Iterative Smooth Backfitting Algorithm
Selects number of functional principal components for given FPCA outpu...
Set the PCA option list
Sparsify densely observed functional data
Compactly display the structure of an FPCA object
Stringing for High-Dimensional data
Trapezoid Rule Numerical Integration
Iterative Smooth Backfitting Algorithm
Sieve estimation: B-spline based estimation procedure for time-varying...
Time-Warping in Functional Data Analysis: Pairwise curve synchronizati...
Simulate a standard Wiener processes (Brownian motions)
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>.