Package: ppseq 0.2.5

Emily C. Zabor

ppseq: Design Clinical Trials using Sequential Predictive Probability Monitoring

Functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

Authors:Emily C. Zabor [aut, cre], Brian P. Hobbs [aut], Michael J. Kane [aut]

ppseq_0.2.5.tar.gz
ppseq_0.2.5.zip(r-4.7-any)ppseq_0.2.5.zip(r-4.6-any)ppseq_0.2.5.zip(r-4.5-any)
ppseq_0.2.5.tgz(r-4.6-any)ppseq_0.2.5.tgz(r-4.5-any)
ppseq_0.2.5.tar.gz(r-4.7-any)ppseq_0.2.5.tar.gz(r-4.6-any)
ppseq_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ppseq/json (API)

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

Bug tracker:https://github.com/zabore/ppseq/issues

Pkgdown/docs site:https://www.emilyzabor.com

Datasets:

On CRAN:

Conda:

5.77 score 5 stars 39 scripts 332 downloads 8 exports 71 dependencies

Last updated from:3a9e5a718a. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK223
source / vignettesOK390
linux-releaseOK222
macos-releaseOK185
macos-oldrelOK165
windows-develOK177
windows-releaseOK284
windows-oldrelOK265
wasm-releaseOK121

Exports:calc_decision_rulescalc_nextcalc_posteriorcalc_predictivecalibrate_posterior_thresholdcalibrate_thresholdsoptimize_designsim_single_trial

Dependencies:askpassbase64encbslibcachemclicodetoolscpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsfurrrfuturegenericsggplot2globalsgluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclelistenvmagrittrmemoisemimeopensslotelparallellypatchworkpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

One-sample expansion cohort
Setup | Introduction | Case study background | Re-design of case study | Using calibrate_thresholds() to obtain design options | Results | print() | optimize_design() | calc_decision_rules() | plot() | References
Rendered fromone_sample_expansion.Rmdusingknitr::rmarkdown

Last update: 2022-10-03
Started: 2021-03-02

Two-sample randomized trial
Setup | Introduction | Case study background | Re-design of case study | Using calibrate_thresholds() to obtain design options | Results | print() | optimize_design() | calc_decision_rules() | plot() | References
Rendered fromtwo_sample_randomized.Rmdusingknitr::rmarkdown

Last update: 2022-10-03
Started: 2021-04-13