vows-package: Voxelwise semiparametrics

In vows: Voxelwise Semiparametrics

Description

This package efficiently performs inference on a large set of parametric or semiparametric regressions that are "parallel" in the sense that they have a common design matrix. The functions are inspired by neuroimaging applications, where the parallel models pertain to a grid of brain locations known as voxels.

Details

Functions ending in ".mp" ("massively parallel") are designed for responses in the form of a (wide) matrix; functions ending in "4d" take four-dimensional response data (e.g., a set of images) and convert it to matrix form so that the corresponding ".mp" function can be applied. Examples include lm.mp and lm4d for ordinary linear models, rlrt.mp and rlrt4d for restricted likelihood ratio tests (RLRTs) of a parametric null hypothesis vs. a smooth alternative, and semipar.mp and semipar4d for smoothing (see Reiss et al., 2014).

Author(s)

Philip Reiss [e-mail], Yin-Hsiu Chen [e-mail], Lei Huang [e-mail], Lan Huo, Ruixin Tan and Rong Jiao [e-mail]

Maintainer: Philip Reiss [e-mail]

References

Reiss, P. T., Huang, L., Chen, Y.-H., Huo, L., Tarpey, T., and Mennes, M. (2014). Massively parallel nonparametric regression, with an application to developmental brain mapping. Journal of Computational and Graphical Statistics, Journal of Computational and Graphical Statistics, 23(1), 232–248.

vows documentation built on May 2, 2019, 9:26 a.m.