Description Details Author(s) References
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.
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).
Philip Reiss phil.reiss@nyumc.org, Yin-Hsiu Chen enjoychen0701@gmail.com, Lei Huang huangracer@gmail.com, Lan Huo, Ruixin Tan and Rong Jiao jiaorong007@gmail.com
Maintainer: Philip Reiss phil.reiss@nyumc.org
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.
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