Package: sprtt 0.3.1

sprtt: Sequential Probability Ratio Tests Toolbox

A toolbox for Sequential Probability Ratio Tests (SPRT) based on Wald (1945) <doi:10.2134/agronj1947.00021962003900070011x>. SPRTs are applied during the sampling process, ideally after each observation, and at every stage return a decision to either continue sampling or terminate and accept one of the specified hypotheses. The `seq_ttest()` function performs one-sample, two-sample, and paired t-tests for one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>). The `seq_anova()` function performs a sequential one-way fixed effects ANOVA (Steinhilber et al. (2024) <doi:10.1037/met0000677>). The `plan_sample_size()` function helps plan sequential studies by simulating required sample sizes across a range of effect sizes. For more information, see the vignettes browseVignettes(package = "sprtt") or the package website <https://meikesteinhilber.github.io/sprtt/>.

Authors:Meike Snijder-Steinhilber [aut, cre], Martin Schnuerch [aut, ths], Anna-Lena Schubert [aut, ths]

sprtt_0.3.1.tar.gz
sprtt_0.3.1.zip(r-4.7)sprtt_0.3.1.zip(r-4.6)sprtt_0.3.1.zip(r-4.5)
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sprtt_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sprtt/json (API)

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

Bug tracker:https://github.com/meikesteinhilber/sprtt/issues

Pkgdown/docs site:https://meikesteinhilber.github.io

Datasets:

On CRAN:

Conda:

sequentialsprttest-statistic

6.52 score 7 stars 27 scripts 292 downloads 10 exports 107 dependencies

Last updated from:371a9c7c75. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK253
source / vignettesOK228
linux-release-x86_64OK209
macos-release-arm64OK220
macos-oldrel-arm64OK210
windows-develOK150
windows-releaseOK202
windows-oldrelOK176
wasm-releaseOK163

Exports:cache_clearcache_infodownload_sample_size_datadraw_sample_mixturedraw_sample_normalload_sample_size_dataplan_sample_sizeplot_anovaseq_anovaseq_ttest

Dependencies:abindarmaskpassbase64encBHbigDbitopsbootbslibcachemclicodacommonmarkcpp11curldigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2ggtextgitcredsgluegridtextgtgtablehighrhtmltoolshtmlwidgetshttrhttr2iniisobandjpegjquerylibjsonlitejuicyjuiceknitrlabelinglatticelavaanlifecyclelitedownlme4lubridatemagrittrmarkdownMASSMatrixMBESSmemoisemimimeminqamnormtmvtnormnlmenloptrnumDerivOpenMxopensslpbivnormpiggybackpillarpkgconfigpngpurrrquadprogR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreactablereactRreformulasrlangrmarkdownrpfS7sassscalessemsemToolsStanHeadersstringistringrsystibbletidyselecttimechangetinytexutf8V8vctrsviridisLitewithrxfunxml2yaml

Developer Guide: sprtt Package
Overview | Package Architecture | Function Structure | Software Testing | Continuous Integration | GitHub Structure | Release Checklist | Pre-release | CRAN Submission | Post-release | Contributing
Rendered fromdeveloper_guide.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2026-04-13

How to use the sprtt package?
Workflow | 1. Understand the theoretical background of SPRTs | 2. When to use SPRTs | 3. Plan your resources | 4. Plan the data collection and register your test specifications | 5. Collect the data and apply the SPRTs | 5. Reporting of the results | References
Rendered fromworkflow_sprtt.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2026-04-13

Sample Size Planning for Sequential ANOVAs
Why Sample Size Planning Matters | Resource Constraints and Decision Rates | The plan_sample_size() Function | Pre-computed Simulation Database | Getting Started | Your First Sample Size Report | Function Parameters | Input Validation | Practical Use Cases | Case 1: Comparing Different Effect Sizes | Case 2: Saving Reports to a Specific Location | Case 3: Preparing Multiple Scenarios | Managing the Simulation Data
Rendered fromplan_sample_size.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2026-04-13

Sequential One-Way ANOVA
Hypotheses | The $F$ Statistic | The Likelihood Ratio | Decision Boundaries | Decision Rule | Efficiency and Robustness | How to use seq_anova() | Step 1: Simulate or Load Data | Step 2: Initial Sequential Analysis | Step 3: Repeated Sequential Testing | Step 4: Final Analysis | How to plot the ANOVA results | Scenario 1: Balanced Data with Perfect Sampling Order | Scenario 2: Unbalanced Data with Imperfect Sampling Order | References
Rendered fromone_way_anova.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2023-06-30

Sequential t-Test
What is the sequential t-test? | How do I use the seq_ttest() function? | x argument: formula input | Two-sample test | One-sample test | x argument: numeric input | Further arguments | Paired | Alternative | Na.rm | Verbose | How do I get access to the results? | References
Rendered fromt_test.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2023-06-30

Introduction to SPRTs
The Sequential Testing Principle | The Likelihood Ratio and Decision Boundaries | Random Sample Size | Why SPRTs Always Stop | Efficiency | The Bias-Efficiency Tradeoff | Practical Considerations | References
Rendered fromsprt.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2026-04-13

Simple t-Test Use Case
Use Case | Hypothesis | Data Analysis | Report Results | References
Rendered fromuse_case.Rmdusingknitr::rmarkdown

Last update: 2026-04-13
Started: 2023-06-30