Package: fPASS 1.0.0.9000

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.

Authors:Salil Koner [aut, cre, cph], Sheng Luo [ctb, fnd]

fPASS_1.0.0.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
fPASS/json (API)

# Install 'fPASS' in R:

install.packages('fPASS', repos = c('https://salilkoner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/salilkoner/fpass/issues

On CRAN:

3.70 score 4 scripts 322 downloads 8 exports 54 dependencies

Last updated from:9798fd57da. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	210
source / vignettes	OK	340
linux-release	OK	218
macos-release	OK	255
macos-oldrel	OK	194
windows-devel	OK	157
windows-release	OK	152
windows-oldrel	OK	136
wasm-release	OK	124

Exports:%>%Extract_Eigencomp_fDA fpca_sc PASS_Proj_Test_ufDA pHotellingT Power_Proj_Test_ufDA Sim_HotellingT_unequal_var Sum_of_Wishart_df

Dependencies:boot brio callr cli crayon desc diffobj dplyr evaluate expm face fs gamm4 generics glue gss jsonlite lattice lifecycle lme4 magrittr MASS Matrix matrixcalc mgcv minqa nlme nloptr otel pillar pkgbuild pkgconfig pkgload praise processx ps purrr R6 rbibutils Rcpp RcppEigen Rdpack reformulas rlang rprojroot stringi stringr testthat tibble tidyselect utf8 vctrs waldo withr

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

Installation | Usage | Case study: Calculating PASS analysis for designing RCT with Longitudinal data with a specific visit times. | Argument specifiction for fPASS::PASS_Proj_Test_ufDA() | Distribution of minimum sample size using fPASS::fpca.sc() function: | Estimate eigenfunctions using fPASS::fpca.sc() function | Estimate eigenfunctions using face::face.sparse() function | Distribution of minimum sample size using face::face.sparse() function with pre-specified number of eigenfunction. | Missing data

Rendered fromfPASS.Rmdusingknitr::rmarkdown

Last update: 2023-07-17
Started: 2023-07-09

Help page	Topics
Extract/estimate eigenfunction from a sparse functional or longitudinal design by simulating from a large number of subjects.	Extract_Eigencomp_fDA
Power and Sample size (PASS) calculation of Two-Sample Projection-based test for sparsely observed univariate functional data.	PASS_Proj_Test_ufDA
CDF of Hotelling-T^2 statistic.	pHotellingT
Power of the Two-sample Projection-based test for functional data with known (or estimated) eigencomponents.	Power_Proj_Test_ufDA
Samples from the non-null distribution of the Hotelling-T^2 statistic under unequal covariance.	Sim_HotellingT_unequal_var
The approximate degrees of freedom formula for sum of Wishart.	Sum_of_Wishart_df

Package: fPASS 1.0.0.9000

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

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Help Manual

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