Package index • BUCSS

t Tests

One-sample, independent, dependent (paired), and unequal-variance (Welch) t test designs, that is, the standardized mean difference.

ss_buc_independent_t() ss_buc_smd(): Necessary Sample Size to Reach Desired Power for an Independent t Test (or Standardized Mean Difference) Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_paired_t() ss_buc_smd_paired(): Necessary Sample Size to Reach Desired Power for a Dependent (Paired) t Test (or Standardized Mean Difference) Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_one_sample_t(): Necessary Sample Size to Reach Desired Power for a One-Sample t Test Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_welch_t(): Necessary Sample Size to Reach Desired Power for a Welch (Unequal Variance) t Test Using a Publication Bias and Uncertainty Correction Procedure

Between-Subjects ANOVA and ANCOVA

One-way, two-way, and general between-subjects designs, with or without covariates, planned per group or per cell.

ss_buc_one_way_anova(): Necessary Sample Size to Reach Desired Power for a One-Way Between-Subjects ANOVA Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_factorial_anova(): Necessary Sample Size to Reach Desired Power for a Two-Way Between-Subjects ANOVA Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_factorial_anova_general(): Necessary Sample Size to Reach Desired Power for a Between-Subjects ANOVA With Any Number of Factors Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_ancova(): Necessary Sample Size to Reach Desired Power for an Analysis of Covariance Using a Publication Bias and Uncertainty Correction Procedure

Within-Subjects ANOVA

Repeated measures designs, planned on the total number of subjects.

ss_buc_rm_anova(): Necessary Sample Size to Reach Desired Power for a One- or Two-Way Within-Subjects ANOVA Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_rm_anova_general(): Necessary Sample Size to Reach Desired Power for a Within-Subjects ANOVA With Any Number of Factors Using a Publication Bias and Uncertainty Correction Procedure

Split-Plot (Mixed) ANOVA

Designs crossing a between-subjects factor with a within-subjects factor.

ss_buc_mixed_anova(): Necessary Sample Size to Reach Desired Power for a Two-Factor Split-Plot (Mixed) ANOVA Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_mixed_anova_general(): 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 Procedure

Correlation and Multiple Regression

A Pearson correlation, a single coefficient, the omnibus model R2, or a joint subset of predictors.

ss_buc_correlation(): Necessary Sample Size to Reach Desired Power for a Pearson Correlation Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_reg_coef(): Necessary Sample Size to Reach Desired Power for a Single Coefficient in a Multiple Regression Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_R2(): Necessary Sample Size to Reach Desired Power for a Test of the Squared Multiple Correlation in a Multiple Regression Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_reg_joint(): 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 Procedure

Multivariate and Latent Variable Designs

A two-group multivariate comparison, and the chi square difference test between nested models.

ss_buc_manova(): Necessary Sample Size to Reach Desired Power for a Two-Group Multivariate Comparison Using a Publication Bias and Uncertainty Correction Procedure
ss_buc_chisq_diff(): Necessary Sample Size to Reach Desired Power for a Nested Model Chi Square Difference Test Using a Publication Bias and Uncertainty Correction Procedure

Seeing and Auditing a Plan

Draw the assurance ceiling a prior result can support, and simulate the literature a plan came from to see how often a plan built this way actually reaches the target power.

plot(<bucss_plan>): Plot the Assurance Ceiling of a Plan
ss_buc_sensitivity(): Simulate the Literature a Plan Came From and Audit How Often It Works
print(<bucss_sensitivity>): Print a Simulated Sensitivity Audit of a Plan

The Result Object

Working with the tidy bucss_plan object the planners return.

print(<bucss_plan>): Print a Bias and Uncertainty Corrected Sample Size Result
tidy(<bucss_plan>) glance(<bucss_plan>): Tidy and Summarize a Bias and Uncertainty Corrected Sample Size Result
planning_sentence(): The Planning Sentence for a Manuscript

Deprecated 1.x Functions

The dot-named 1.x API, kept working as thin wrappers around the ss_buc_* planners.

ss.power.it() ss.power.dt() ss.power.ba() ss.power.ba.general() ss.power.wa() ss.power.wa.general() ss.power.spa() ss.power.spa.general() ss.power.reg1() ss.power.reg.all() ss.power.reg.joint(): Deprecated 1.x Functions in BUCSS

Package Overview

The package-level help page, with the method and its references.

BUCSS BUCSS-package: BUCSS: Bias and Uncertainty Corrected Sample Size