fPASS: Power and Sample Size for Projection Test under Repeated Measures

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

Version: 1.0.0
Imports: dplyr, purrr, face, magrittr, MASS, Matrix, nlme, testthat, mgcv, lifecycle, expm, gamm4, gss, rlang, stringr, utils
Suggests: knitr, rmarkdown, Hotelling, refund, foreach
Published: 2023-07-19
Author: Salil Koner ORCID iD [aut, cre, cph], Sheng Luo [ctb, fnd]
Maintainer: Salil Koner <salil.koner at duke.edu>
BugReports: https://github.com/SalilKoner/fPASS/issues
License: MIT + file LICENSE
URL: https://github.com/SalilKoner/fPASS
NeedsCompilation: no
Materials: README NEWS
CRAN checks: fPASS results

Documentation:

Reference manual: fPASS.pdf
Vignettes: fPASS: An R package for Power and Sample Size analysis (PASS) for Projection-based Two-Sample test for functional data.

Downloads:

Package source: fPASS_1.0.0.tar.gz
Windows binaries: r-devel: fPASS_1.0.0.zip, r-release: fPASS_1.0.0.zip, r-oldrel: fPASS_1.0.0.zip
macOS binaries: r-release (arm64): fPASS_1.0.0.tgz, r-oldrel (arm64): fPASS_1.0.0.tgz, r-release (x86_64): fPASS_1.0.0.tgz

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