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CLAST (version 1.0.1)

Exact Confidence Limits after a Sequential Trial

Description

The user first provides design vectors n, a and b as well as null (p0) and alternative (p1) benchmark values for the probability of success. The key function "mv.plots.SM()" calculates mean values of exact upper and lower limits based on four different rank ordering methods. These plots form the basis of selecting a rank ordering. The function "inference()" calculates exact limits from a provided realisation and ordering choice. For more information, see "Exact confidence limits after a group sequential single arm binary trial" by Lloyd, C.J. (2020), Statistics in Medicine, Volume 38, 2389-2399, .

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Version

Install

install.packages('CLAST')

Monthly Downloads

236

Version

1.0.1

License

GPL-2

Maintainer

Chris Lloyd

Last Published

April 8th, 2022

Functions in CLAST (1.0.1)

mv.plots.SM

Diagnostic mean values plots.
errors.SM

Exact error rates of specified sequential design
sample.space.SM

Sample space enumeration
cross

Special combination of Matrix and Vector
sample.space

Sample space enumeration (K>2)
inference

Exact limits from outcome
mv.SM

Mean value of upper limits.
exact.lower.limits.SM

Calculates all exact lower limits.
exact.upper.limits.SM

Calculates all exact upper limits.
LR.upper

Calculates likelihood ratio based upper limit
plt.sample.space.SM

Sample space for given sequential design.
sample.space.2

Sample space enumeration (K=2)
prob.SM

Probability of sufficient statistics (S,M).
JT.rank.SM

Calculates Jennison & Turnbull ranking of sample space
LR.lower

Calculates likelihood ratio based lower limit
CLAST-package

Confidence Limits After Sequential Trial CLAST
ML.rank.SM

Maximum likelihood estimator of p.
CP.upper

Calculates Clopper-Pearson upper limit
CP.stats.SM

Calculates all possible Clopper-Pearson limits.
RANK

Produces ranks of entries of vector
LR.stats.SM

Calculates all possible LR limits.
CP.lower

Calculates Clopper-Pearson lower limit