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fPASS: Power and Sample Size Analysis for Projection-Based Testing of Mean Difference under Repeated Measures Design

Salil Koner

The details of the power and sample size formula and the relevant computational details are documented in the manuscript. The users are encourage to see Wang (2021) and Koner and Luo (2023) for further details about the testing procedure.

fPASS is designed to make it quick and easy software for randomized clinical trial simulation tool for determining treatment efficacy where the response collected under a longitudinal or functional design. The current development version of the package can be installed by running the following.

Installation

# Install development version from GitHub
devtools::install_github("SalilKoner/fPASS") # Vignettes takes about 20 minutes to run. 

Vignettes

If you want to install the package with the vignettes to be built, then run

# Install development version from GitHub with the vignettes.
# Vignettes takes about 5-7 minutes to run. 
devtools::install_github("SalilKoner/fPASS", build_vignettes = TRUE) 

followed by browseVignettes("fPASS") to see the application of the package in real life case studies.

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Install

install.packages('fPASS')

Monthly Downloads

159

Version

1.0.0

License

MIT + file LICENSE

Issues

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Maintainer

Salil Koner

Last Published

July 19th, 2023

Functions in fPASS (1.0.0)

fpca_sc

Functional principal components analysis by smoothed covariance
Extract_Eigencomp_fDA

Extract/estimate eigenfunction from a sparse functional or longitudinal design by simulating from a large number of subjects.
Sim_HotellingT_unequal_var

Samples from the non-null distribution of the Hotelling-\(T^2\) statistic under unequal covariance.
Sum_of_Wishart_df

The approximate degrees of freedom formula for sum of Wishart.
PASS_Proj_Test_ufDA

Power and Sample size (PASS) calculation of Two-Sample Projection-based test for sparsely observed univariate functional data.
%>%

Pipe operator
pHotellingT

CDF of Hotelling-\(T^2\) statistic.
Power_Proj_Test_ufDA

Power of the Two-sample Projection-based test for functional data with known (or estimated) eigencomponents.
fPASS-package

fPASS package