fcr: Functional Concurrent Regression for Sparse Data

Dynamic prediction in functional concurrent regression with an application to child growth. Extends the pffr() function from the 'refund' package to handle the scenario where the functional response and concurrently measured functional predictor are irregularly measured. Leroux et al. (2017), Statistics in Medicine, <doi:10.1002/sim.7582>.

Version:	1.0
Depends:	R (≥ 3.2.4), face (≥ 0.1), mgcv (≥ 1.7), fields (≥ 9.0)
Suggests:	knitr, rmarkdown
Published:	2018-03-13
Author:	Andrew Leroux [aut, cre], Luo Xiao [aut, cre], Ciprian Crainiceanu [aut], William Checkly [aut]
Maintainer:	Andrew Leroux <aleroux2 at jhu.edu>
License:	GPL (≥ 3)
NeedsCompilation:	no
CRAN checks:	fcr results

Documentation:

Reference manual:	fcr.pdf
Vignettes:	Tutorial on how to fit models and perform prediction using fcr

Downloads:

Package source:	fcr_1.0.tar.gz
Windows binaries:	r-devel: fcr_1.0.zip, r-release: fcr_1.0.zip, r-oldrel: fcr_1.0.zip
macOS binaries:	r-release (arm64): fcr_1.0.tgz, r-oldrel (arm64): fcr_1.0.tgz, r-release (x86_64): fcr_1.0.tgz, r-oldrel (x86_64): fcr_1.0.tgz

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