Package: BUCSS 2.0.0

BUCSS: Bias and Uncertainty Corrected Sample Size

Implements sample size planning for a future study from the summary statistics of a prior study, correcting the prior effect for publication bias and uncertainty so the planned study attains its target statistical power more reliably than methods that take a sample effect size at face value. Covers the independent and dependent 't' test, between-subjects, within-subjects, and split-plot factorial analysis of variance, and multiple regression. The method is described in Anderson, Kelley, and Maxwell (2017) <doi:10.1177/0956797617723724>. The noncentrality parameter underlying the correction is obtained by direct numerical root finding, a more accurate approach than a fixed grid search that also removes an internal upper limit on the parameter. A small number of planned sample sizes therefore differ slightly from versions 1.x; the new values are more accurate.

Authors:Ken Kelley [aut, cre], Samantha F. Anderson [aut], Scott E. Maxwell [aut]

BUCSS_2.0.0.tar.gz
BUCSS_2.0.0.zip(r-4.7-any)BUCSS_2.0.0.zip(r-4.6-any)BUCSS_2.0.0.zip(r-4.5-any)
BUCSS_2.0.0.tgz(r-4.6-any)BUCSS_2.0.0.tgz(r-4.5-any)
BUCSS_2.0.0.tar.gz(r-4.7-any)BUCSS_2.0.0.tar.gz(r-4.6-any)
BUCSS_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BUCSS/json (API)

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

Bug tracker:https://github.com/yelleknek/bucss/issues

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

On CRAN:

Conda:

6.25 score 37 scripts 407 downloads 2 mentions 35 exports 1 dependencies

Last updated from:4c511c43bc. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK129
source / vignettesOK195
linux-releaseOK113
macos-releaseOK176
macos-oldrelOK178
windows-develOK102
windows-releaseOK92
windows-oldrelOK76
wasm-releaseOK95

Exports:glanceplanning_sentencess_buc_ancovass_buc_chisq_diffss_buc_correlationss_buc_factorial_anovass_buc_factorial_anova_generalss_buc_independent_tss_buc_manovass_buc_mixed_anovass_buc_mixed_anova_generalss_buc_one_sample_tss_buc_one_way_anovass_buc_paired_tss_buc_R2ss_buc_reg_coefss_buc_reg_jointss_buc_rm_anovass_buc_rm_anova_generalss_buc_sensitivityss_buc_smdss_buc_smd_pairedss_buc_welch_tss.power.bass.power.ba.generalss.power.dtss.power.itss.power.reg.allss.power.reg.jointss.power.reg1ss.power.spass.power.spa.generalss.power.wass.power.wa.generaltidy

Dependencies:generics

Planning a Correlation Study
A Correlation Is Not a Regression Slope | Assuming Less Publication Bias | Auditing the Plan by Simulation | When the Prior Result Cannot Support the Request | References
Rendered fromcorrelation.Rmdusingknitr::rmarkdown

Last update: 2026-07-30
Started: 2026-07-30

Assurance and the Publication Bias Correction
Assurance: Correcting for Uncertainty | When the Correction Cannot Be Made | The Ceiling, Drawn | alpha_prior: Modeling Publication Bias | Seeing the Assurance Claim Exercised | Practical Guidance | References
Rendered fromunderstanding-assurance.Rmdusingknitr::rmarkdown

Last update: 2026-07-29
Started: 2026-05-31

Worked Examples Across Designs
Independent t Test | Dependent (Paired) t Test | Between-Subjects ANOVA | Within-Subjects ANOVA | Split-Plot (Mixed) ANOVA | Multiple Regression | Correlation | Analysis of Covariance | Multivariate Comparison of Two Groups | Nested Model Comparison | Unequal Variances | References
Rendered fromworked-examples.Rmdusingknitr::rmarkdown

Last update: 2026-07-29
Started: 2026-05-31

Getting Started With BUCSS
Why Not Plan From the Observed Effect Size? | What BUCSS Does | A First Example | Reading the Result | The Replication Example | The Function Family | References
Rendered fromBUCSS.Rmdusingknitr::rmarkdown

Last update: 2026-07-26
Started: 2026-05-31

Planning Replication Studies
A Literature Built From Underpowered Studies | Why Planning at Face Value Underpowers the Replication | The BUCSS Correction | Reading and Using the Result | The Bigger Picture | References
Rendered fromplanning-replications.Rmdusingknitr::rmarkdown

Last update: 2026-07-26
Started: 2026-06-28

Reading and Using BUCSS Output
1. Read It as a Printed Summary | 2. Pull a Single Number Out | 3. The Broom Verbs: tidy() and glance() | What Lives Where | References
Rendered fromreading-output.Rmdusingknitr::rmarkdown

Last update: 2026-07-26
Started: 2026-06-04

Readme and manuals

Help Manual

Help pageTopics
BUCSS: Bias and Uncertainty Corrected Sample SizeBUCSS-package BUCSS
Tidy and summarize a bias and uncertainty corrected sample size resultbucss_tidiers glance.bucss_power tidy.bucss_power
The planning sentence for a manuscriptplanning_sentence
Plot the assurance ceiling of a planplot.bucss_power
Necessary sample size to reach desired power for an analysis of covariance using a publication bias and uncertainty correction proceduress_buc_ancova
Necessary sample size to reach desired power for a nested model chi-square difference test using a publication bias and uncertainty correction proceduress_buc_chisq_diff
Necessary sample size to reach desired power for a Pearson correlation using a publication bias and uncertainty correction proceduress_buc_correlation
Necessary sample size to reach desired power for a two-way between-subjects ANOVA using a publication bias and uncertainty correction proceduress_buc_factorial_anova
Necessary sample size to reach desired power for a between-subjects ANOVA with any number of factors using a publication bias and uncertainty correction proceduress_buc_factorial_anova_general
Necessary sample size to reach desired power for an independent t test (or standardized mean difference) using a publication bias and uncertainty correction proceduress_buc_independent_t ss_buc_smd
Necessary sample size to reach desired power for a two-group multivariate comparison using a publication bias and uncertainty correction proceduress_buc_manova
Necessary sample size to reach desired power for a two-factor split-plot (mixed) ANOVA using a publication bias and uncertainty correction proceduress_buc_mixed_anova
Necessary sample size to reach desired power for a split-plot (mixed) ANOVA with any number of factors using a publication bias and uncertainty correction proceduress_buc_mixed_anova_general
Necessary sample size to reach desired power for a one-sample t test using a publication bias and uncertainty correction proceduress_buc_one_sample_t
Necessary sample size to reach desired power for a one-way between-subjects ANOVA using a publication bias and uncertainty correction proceduress_buc_one_way_anova
Necessary sample size to reach desired power for a dependent (paired) t test (or standardized mean difference) using a publication bias and uncertainty correction proceduress_buc_paired_t ss_buc_smd_paired
Necessary sample size to reach desired power for a test of model R^2 in a multiple regression using a publication bias and uncertainty correction proceduress_buc_R2
Necessary sample size to reach desired power for a single coefficient in a multiple regression using a publication bias and uncertainty correction proceduress_buc_reg_coef
Necessary sample size to reach desired power for a joint test of multiple predictors in a multiple regression using a publication bias and uncertainty correction proceduress_buc_reg_joint
Necessary sample size to reach desired power for a one or two-way within-subjects ANOVA using a publication bias and uncertainty correction proceduress_buc_rm_anova
Necessary sample size to reach desired power for a within-subjects ANOVA with any number of factors using a publication bias and uncertainty correction proceduress_buc_rm_anova_general
Simulate the literature a plan came from and audit how often it worksss_buc_sensitivity
Necessary sample size to reach desired power for a Welch (unequal variance) t test using a publication bias and uncertainty correction proceduress_buc_welch_t