Wiener

A vector of points in [0, 1] specifying the support of the processes.

A vector of integers. The number of observations per curve will be uniform distribution on sparsify.

sparsify

Simulate <code>n</code> standard Wiener processes on [0, 1], possibly
sparsifying the results.

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 or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Wiener: Simulate standard Wiener processes (Brownian motions)

Description

Usage

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Value

Details

See Also