ate_from_bv(): Calculate allowable total error (ATE)
specifications from biological variation data using the Fraser-Petersen
model. Supports three performance levels (optimal, desirable, minimum)
and provides allowable imprecision, allowable bias, and total allowable
error specifications.
sigma_metric(): Calculate the Six Sigma metric for
analytical performance assessment. Returns sigma value with
interpretation category (World Class to Unacceptable) and approximate
defect rates.
ate_assessment(): Comprehensive evaluation of
observed method performance against allowable total error
specifications. Provides pass/fail assessment for individual components
(bias, CV, total error) and overall method acceptability, integrated
with sigma metric calculation.
deming_regression(): Deming regression for method
comparison, accounting for measurement error in both variables. Supports
known error ratio or estimation from replicates. Includes jackknife and
bootstrap BCa confidence intervals.
S3 methods for Deming regression: print(),
summary(), plot(), and autoplot()
(ggplot2).
New vignette: “Deming Regression for Method Comparison” – comprehensive guide to Deming regression theory and practical application.
Updated vignette: “Understanding Method Comparison Statistics” – added guidance on choosing between regression methods.
Initial CRAN release.
ba_analysis(): Bland-Altman method comparison
analysis with bias estimation, limits of agreement, and confidence
intervals. Supports both absolute and percentage difference
scaling.
pb_regression(): Passing-Bablok regression with fast
O(n log n) algorithm via the robslopes package. Includes analytical
confidence intervals (Passing & Bablok 1983) and optional bootstrap
BCa intervals. CUSUM test for linearity assessment with
Kolmogorov-Smirnov p-value.
S3 methods for both analyses: print(),
summary(), plot(), and autoplot()
(ggplot2).
Publication-ready visualizations using ggplot2, including Bland-Altman plots, regression scatter plots with confidence bands, residual plots, and CUSUM plots for linearity assessment.
glucose_methods: Point-of-care glucose meter vs
laboratory analyzer (n=60)
creatinine_serum: Enzymatic vs Jaffe creatinine
methods (n=80)
troponin_cardiac: Two high-sensitivity cardiac
troponin I platforms (n=50)
Vignette: “Method Comparison Workflow” – step-by-step analysis guide
Vignette: “Understanding Method Comparison Statistics” – educational overview of statistical concepts for method comparison studies