Package: fcr 1.0

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>.

Authors:Andrew Leroux [aut, cre], Luo Xiao [aut, cre], Ciprian Crainiceanu [aut], William Checkly [aut]

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fcr.pdf |fcr.html✨
fcr/json (API)

# Install 'fcr' in R:
install.packages('fcr', repos = c('https://andrew-leroux.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.15 score 14 scripts 144 downloads 1 mentions 1 exports 12 dependencies

Last updated 7 years agofrom:98d8dcd75e. Checks:3 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 13 2025
R-4.5-winNOTEMar 13 2025
R-4.5-macNOTEMar 13 2025
R-4.5-linuxNOTEMar 13 2025
R-4.4-winNOTEMar 13 2025
R-4.4-macNOTEMar 13 2025
R-4.4-linuxNOTEMar 13 2025
R-4.3-winOKMar 13 2025
R-4.3-macOKMar 13 2025

Exports:fcr

Dependencies:dotCall64facefieldslatticemapsMatrixmatrixcalcmgcvnlmeRcppspamviridisLite

Dynamic prediction in functional concurrent regression with sparse functional covariates

Leroux A, Xiao L, Crainiceanu C, Checkley W

Rendered fromdynamic-prediction.Rmdusingknitr::rmarkdownon Mar 13 2025.

Last update: 2018-03-13
Started: 2018-03-13