Changes in version 0.4.1 (2026-02-19) Bug Fixes Fixed troponin_precision dataset which was generated with constant values (zero variance) at each concentration level. The dataset now contains realistic variability following a hyperbolic CV model, enabling proper demonstration of precision_study() and precision_profile() functions. Documentation Updated README with precision experiments examples. Changes in version 0.4.0 (2026-02-09) New Features Precision Experiments (EP05/EP15-aligned) - precision_study(): Comprehensive variance component analysis for precision experiments with nested experimental designs. - Supports multiple design types: single-site (day/run/replicate), multi-site (site/day/run/replicate), and custom nested designs - Automatic design detection from data structure - Two estimation methods: ANOVA (method of moments) and REML (via lme4) - Three confidence interval methods: Satterthwaite (default), Modified Large Sample (MLS), and bootstrap BCa - Multi-sample support for analyzing multiple concentration levels - Complete S3 methods: print(), summary(), plot(), autoplot() - verify_precision(): Statistical verification of observed precision against manufacturer claims using chi-square hypothesis testing. - Accepts numeric vectors, precision_study objects, or data frames - Calculates Upper Verification Limit (UVL) - Provides confidence intervals for observed precision - Clear pass/fail determination with detailed interpretation - precision_profile(): Models the relationship between CV and concentration for functional sensitivity estimation. - Hyperbolic model: CV = sqrt(a² + (b/x)²) (default) - Linear model: CV = a + b/x - Calculates functional sensitivity at user-specified CV targets - Optional bootstrap confidence intervals for functional sensitivity - Integrates with precision_study objects for seamless workflow Precision Measures The package now calculates and reports: - Repeatability: Within-run precision - Between-run precision: Additional variability between runs within a day - Between-day precision: Additional variability between days - Within-laboratory precision: Combined day + run + error variance - Between-site precision: Additional variability between sites (multi-site only - Reproducibility: Total precision including all variance components Visualization - plot.precision_study() with three plot types: - type = "variance": Variance component bar chart - type = "cv": CV profile across precision measures with CIs - type = "precision": Forest plot of precision estimates - plot.precision_profile(): Publication-ready precision profile visualization - Fitted curve with prediction intervals - Functional sensitivity target lines - Optional logarithmic x-axis scale New Dataset - troponin_precision: High-sensitivity cardiac troponin I precision study data with 6 concentration levels (5-500 ng/L), designed for demonstrating precision_study() and precision_profile() workflows. Documentation - New vignette: "Precision Profiles and Functional Sensitivity" - demonstrates the complete workflow from raw precision data to functional sensitivity estimation with clinical interpretation. Dependencies - lme4 added to Suggests for REML estimation (optional) Changes in version 0.3.0 (2026-01-22) Initial CRAN release. New features - 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. Documentation - New vignette: "Setting Quality Goals with Biological Variation" -- explains the biological variation model for analytical performance specifications with practical examples using the new ATE functions. Changes in version 0.2.0 New features - 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). Documentation - 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. Changes in version 0.1.0 New features - 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. Datasets - 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) Documentation - Vignette: "Method Comparison Workflow" -- step-by-step analysis guide - Vignette: "Understanding Method Comparison Statistics" -- educational overview of statistical concepts for method comparison studies