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valytics 0.4.0

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)

valytics 0.3.0

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.

valytics 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.

valytics 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.