Package index • measure

Input Steps

Convert raw data to measure’s internal format

step_measure_input_wide(): Ingest Measurements in Separate Columns
step_measure_input_long(): Ingest Measurements from a Single Column

Processing Steps

Apply spectral preprocessing transformations

step_measure_savitzky_golay(): Savitzky-Golay Pre-Processing
step_measure_snv(): Standard Normal Variate (SNV) Transformation
step_measure_msc(): Multiplicative Scatter Correction (MSC)
step_measure_map(): Apply a Custom Function to Measurements

Multi-Channel Operations

Align, combine, and compute ratios between measurement channels

step_measure_channel_align(): Align Multiple Channels to a Common Grid
step_measure_channel_combine(): Combine Multiple Channels
step_measure_channel_ratio(): Compute Ratios Between Channels

Spectral Math

Mathematical transformations for spectral data

step_measure_absorbance(): Convert Transmittance to Absorbance
step_measure_transmittance(): Convert Absorbance to Transmittance
step_measure_log(): Log Transformation
step_measure_kubelka_munk(): Kubelka-Munk Transformation
step_measure_derivative(): Simple Finite Difference Derivatives
step_measure_derivative_gap(): Gap (Norris-Williams) Derivatives

Region Operations

Trim, exclude, and resample measurement regions

step_measure_trim(): Trim Measurements to Specified Range
step_measure_exclude(): Exclude Measurement Ranges
step_measure_resample(): Resample Measurements to New Grid
step_measure_interpolate(): Interpolate Gaps in Measurement Data

Sample-wise Normalization

Normalize each spectrum independently

step_measure_normalize_auc(): Normalize by Area Under Curve
step_measure_normalize_istd(): Internal Standard Normalization
step_measure_normalize_max(): Normalize by Maximum Value
step_measure_normalize_peak(): Normalize to a Specific Peak Region
step_measure_normalize_range(): Normalize to Range 0-1
step_measure_normalize_sum(): Normalize by Sum (Total Intensity)
step_measure_normalize_vector(): Normalize by L2 (Euclidean) Norm

Variable-wise Scaling

Scale across samples at each measurement location

step_measure_center(): Mean Centering
step_measure_scale_auto(): Auto-Scaling (Z-Score Normalization)
step_measure_scale_pareto(): Pareto Scaling
step_measure_scale_range(): Range Scaling
step_measure_scale_vast(): VAST Scaling (Variable Stability Scaling)

Reference-Based Corrections

Blank subtraction and reference corrections

step_measure_subtract_blank(): Subtract Blank Measurement
step_measure_subtract_reference(): Subtract or Divide by Reference Spectrum
step_measure_ratio_reference(): Compute Ratio to Reference Spectrum

Calibration

X-axis and Y-axis calibration steps

step_measure_calibrate_x(): Apply X-Axis Calibration
step_measure_calibrate_y(): Apply Y-Axis Calibration (Response Factor)

Output Steps

Convert internal format back to modeling-ready data

step_measure_output_wide(): Reorganize Measurements to Separate Columns
step_measure_output_long(): Reorganize Measurements to Long Format

Peak Operations

Peak detection, integration, and analysis for chromatography

step_measure_peaks_detect(): Detect Peaks in Measurements
step_measure_peaks_integrate(): Integrate Peak Areas
step_measure_peaks_filter(): Filter Peaks by Criteria
step_measure_peaks_deconvolve(): Deconvolve Overlapping Peaks
step_measure_peaks_to_table(): Convert Peaks to Tidy Table
is_peaks_list(): Test if object is a peaks list
find_peaks_cols(): Find peaks columns in a data frame

Smoothing & Noise Reduction

Noise reduction and spike removal steps

step_measure_smooth_ma(): Moving Average Smoothing
step_measure_smooth_median(): Median Filter Smoothing
step_measure_smooth_gaussian(): Gaussian Kernel Smoothing
step_measure_smooth_wavelet(): Wavelet Denoising
step_measure_filter_fourier(): Fourier Low-Pass Filtering
step_measure_despike(): Remove Spikes and Outliers from Measurements

Alignment & Registration

Spectral alignment and shift correction

step_measure_align_shift(): Shift Alignment via Cross-Correlation
step_measure_align_reference(): Align to Reference Spectrum
step_measure_align_dtw(): Dynamic Time Warping Alignment
step_measure_align_ptw(): Parametric Time Warping Alignment
step_measure_align_cow(): Correlation Optimized Warping Alignment

Quality Control

Quality metrics and data cleaning

step_measure_qc_snr(): Calculate Signal-to-Noise Ratio
step_measure_qc_saturated(): Detect Saturated Measurements
step_measure_qc_outlier(): Detect Outlier Samples
step_measure_impute(): Impute Missing Values in Measurements

Tunable Parameters

Parameters for hyperparameter tuning with dials

window_side() differentiation_order(): Parameter for measure steps
baseline_lambda() baseline_asymmetry() baseline_degree() baseline_half_window() baseline_span(): Parameters for baseline correction steps
peak_location_min() peak_location_max(): Parameters for peak normalization
derivative_order() derivative_gap() derivative_segment(): Parameters for derivative steps
smooth_window() smooth_sigma() fourier_cutoff() despike_threshold(): Parameters for smoothing steps
align_max_shift() align_segment_length(): Parameters for alignment steps
outlier_threshold(): Parameters for quality control steps
bin_width() emsc_degree() osc_n_components(): Parameters for feature engineering and scatter correction

Data

Included datasets for examples and testing

meats_long: Fat, water and protein content of meat samples
glucose_bioreactors bioreactors_large bioreactors_small: Raman Spectra Bioreactor Data
hplc_chromatograms: Simulated HPLC Chromatography Data
sec_chromatograms: Simulated SEC/GPC Chromatography Data
sec_calibration: SEC/GPC Calibration Standards Summary
maldi_spectra: Simulated MALDI-TOF Mass Spectrometry Data

Baseline Correction

Baseline correction steps and utilities

step_measure_baseline_airpls(): Adaptive Iteratively Reweighted Penalized Least Squares Baseline
step_measure_baseline_als(): Asymmetric Least Squares (ALS) Baseline Correction
step_measure_baseline_arpls(): Asymmetrically Reweighted Penalized Least Squares Baseline Correction
step_measure_baseline_auto(): Automatic Baseline Correction Method Selection
step_measure_baseline_custom(): Custom Baseline Correction with User-Provided Function
step_measure_baseline_gpc() superseded: GPC/SEC Baseline Correction
step_measure_baseline_minima(): Local Minima Interpolation Baseline Correction
step_measure_baseline_morph(): Iterative Morphological Baseline Correction
step_measure_baseline_poly(): Polynomial Baseline Correction
step_measure_baseline_py(): Python-Based Baseline Correction via pybaselines
step_measure_baseline_rf(): Robust Fitting Baseline Correction
step_measure_baseline_rolling(): Rolling Ball Baseline Correction
step_measure_baseline_snip(): SNIP Baseline Correction
step_measure_baseline_tophat(): Top-Hat Morphological Baseline Correction
step_measure_detrend(): Remove Trend from Measurements
subtract_rf_baseline(): Subtract baseline using robust fitting method

SEC/GPC Analysis

Molecular weight calculations for size exclusion chromatography

step_measure_mw_averages() superseded: Calculate Molecular Weight Averages for SEC/GPC
step_measure_mw_distribution() superseded: Generate Molecular Weight Distribution Curve
step_measure_mw_fractions() superseded: Calculate Molecular Weight Fractions for SEC/GPC

Feature Engineering

Extract features from spectral data

step_measure_integrals(): Calculate Region Integrals
step_measure_ratios(): Calculate Region Ratios
step_measure_moments(): Calculate Statistical Moments
step_measure_bin(): Spectral Binning

Extended Scatter Correction

Advanced scatter correction methods

step_measure_emsc(): Extended Multiplicative Scatter Correction (EMSC)
step_measure_osc(): Orthogonal Signal Correction (OSC)

Data Augmentation

Training data augmentation for improved model robustness

step_measure_augment_noise(): Add Random Noise to Measurements
step_measure_augment_shift(): Add Random X-axis Shifts
step_measure_augment_scale(): Random Intensity Scaling

Drift & Batch Correction

Correct for signal drift and batch effects

step_measure_drift_qc_loess(): QC-Based Drift Correction Using LOESS
step_measure_drift_linear(): Linear Drift Correction
step_measure_drift_spline(): Spline-Based Drift Correction
step_measure_qc_bracket(): QC Bracketing Interpolation
step_measure_batch_reference(): Reference-Based Batch Correction
measure_detect_drift(): Detect Drift in Analytical Data

Calibration & Quantitation

Calibration curves, LOD/LOQ, and quantitation

measure_calibration measure_calibration-class: Calibration Curve Object
measure_calibration_fit(): Fit a Calibration Curve
measure_calibration_predict(): Predict Concentrations from Calibration Curve
measure_calibration_verify(): Verify Calibration Curve Performance
is_measure_calibration(): Test if Object is a Calibration Curve
tidy(<measure_calibration>) tidy(<measure_calibration_verify>): Tidy a Calibration Curve
glance(<measure_calibration>): Glance at Calibration Curve Summary
augment(<measure_calibration>): Augment Calibration Data
measure_lod(): Calculate Limit of Detection (LOD)
measure_loq(): Calculate Limit of Quantitation (LOQ)
measure_lod_loq(): Calculate LOD and LOQ Together
tidy(<measure_lod>): Tidy LOD/LOQ Results

Precision & Accuracy

Method validation for precision and accuracy

measure_repeatability(): Repeatability (Within-Run Precision)
measure_intermediate_precision(): Intermediate Precision (Between-Run Precision)
measure_reproducibility(): Reproducibility (Between-Lab Precision)
measure_gage_rr(): Gage R&R (Measurement System Analysis)
measure_accuracy(): Accuracy Assessment
measure_linearity(): Linearity Assessment
measure_carryover(): Carryover Assessment

Uncertainty

ISO GUM uncertainty budgets

measure_uncertainty_budget(): Create an Uncertainty Budget
measure_uncertainty(): Quick Uncertainty Calculation
uncertainty_component(): Create an Uncertainty Component
uncertainty_type_a(): Create Type A Uncertainty from Repeated Measurements
uncertainty_type_b_rectangular(): Create Type B Uncertainty from Rectangular Distribution
uncertainty_type_b_expanded(): Create Type B Uncertainty from Expanded Uncertainty
tidy(<measure_uncertainty_budget>): Tidy an Uncertainty Budget

Control Charts & QC

Statistical process control and system suitability

measure_control_limits(): Calculate Control Limits
measure_control_chart(): Generate Control Chart
measure_system_suitability(): System Suitability Check

Method Comparison

Compare analytical methods using Bland-Altman, Deming, and Passing-Bablok

measure_bland_altman(): Bland-Altman Method Comparison
measure_deming_regression(): Deming Regression for Method Comparison
measure_passing_bablok(): Passing-Bablok Regression for Method Comparison
measure_proficiency_score(): Proficiency Testing Scores

Matrix Effects & Sample Prep

Matrix effect quantification, standard addition, and sample prep QC

measure_matrix_effect(): Matrix Effect Analysis
step_measure_standard_addition(): Standard Addition Correction
step_measure_dilution_correct(): Dilution Factor Correction
step_measure_surrogate_recovery(): Surrogate/Internal Standard Recovery

Criteria & Assessment

Acceptance criteria and pass/fail evaluation

criterion(): Create an Acceptance Criterion
measure_criteria(): Create a Set of Acceptance Criteria
measure_assess(): Assess Data Against Acceptance Criteria
all_pass(): Check if All Criteria Pass
get_failures(): Extract Failed Criteria
criteria_bioanalytical() criteria_ich_q2() criteria_bland_altman() criteria_method_comparison() criteria_proficiency_testing() criteria_matrix_effects() criteria_surrogate_recovery(): Preset Acceptance Criteria

Metadata Validation

Validate analytical metadata columns

measure_validate_metadata(): Validate Analytical Metadata
measure_standardize_sample_type(): Standardize Sample Type Values
measure_sample_types: Canonical Sample Types

Multi-Way Analysis

PARAFAC, Tucker, and MCR-ALS decomposition for multi-dimensional data

step_measure_parafac(): PARAFAC Decomposition for Multi-Dimensional Data
step_measure_tucker(): Tucker Decomposition for Multi-Dimensional Data
step_measure_mcr_als(): MCR-ALS Decomposition for Multi-Dimensional Data

Multi-Dimensional Data

Support for 2D and higher-dimensional measurement data (LC-DAD, EEM, 2D NMR)

new_measure_nd_tbl(): Create a new n-dimensional measure tibble
new_measure_nd_list(): Create a new n-dimensional measure list
is_measure_nd_tbl(): Test if object is an n-dimensional measure tibble
is_measure_nd_list(): Test if object is an n-dimensional measure list
measure_ndim(): Get the number of dimensions of a measurement
measure_dim_names(): Get dimension names of an n-dimensional measurement
measure_dim_units(): Get dimension units of an n-dimensional measurement
measure_is_regular(): Check if an n-dimensional measurement has a regular grid
measure_grid_info(): Get grid information for an n-dimensional measurement
measure_apply(): Apply a function to measurement data along dimensions
measure_unfold(): Unfold n-dimensional measurement to 1D
measure_fold(): Fold 1D measurement back to n-dimensional
measure_slice(): Extract slices from n-dimensional measurement
measure_project(): Project n-dimensional measurement by aggregating across dimensions
find_measure_nd_cols(): Find n-dimensional measure columns in a data frame
get_measure_col_ndim(): Get the dimensionality of a measure column

Utilities

Helper functions and utilities

new_measure_tbl(): Create a new measure tibble
new_measure_list(): Create a new measure list
is_measure_tbl(): Test if object is a measure tibble
is_measure_list(): Test if object is a measure list
find_measure_cols(): Find measure columns in a data frame
has_measure_col(): Check if data frame has measure column(s)
required_pkgs(<step_measure_align_dtw>) required_pkgs(<step_measure_align_ptw>) required_pkgs(<step_measure_augment_noise>) required_pkgs(<step_measure_augment_shift>) required_pkgs(<step_measure_augment_scale>) required_pkgs(<step_measure_integrals>) required_pkgs(<step_measure_ratios>) required_pkgs(<step_measure_moments>) required_pkgs(<step_measure_bin>) required_pkgs(<step_measure_mw_averages>) required_pkgs(<step_measure_mw_fractions>) required_pkgs(<step_measure_mw_distribution>) required_pkgs(<step_measure_resample>) required_pkgs(<step_measure_savitzky_golay>) required_pkgs(<step_measure_emsc>) required_pkgs(<step_measure_osc>) required_pkgs(<step_measure_smooth_wavelet>): Set package dependencies

Technique Pack Registry

Registration and discovery for technique packs

measure_packs(): List Registered Technique Packs
measure_steps(): List Available Steps
register_measure_pack(): Register a Technique Pack
register_measure_step(): Register a Step from a Technique Pack

Exploration & Analysis

Interactive exploration and summary functions for prototyping

measure_map(): Apply a Function to Each Sample's Measurements
measure_map_safely(): Apply a Function Safely to Each Sample's Measurements
measure_summarize(): Summarize Measurements Across Samples

Data Organization

Column detection and role assignment helpers

measure_identify_columns(): Identify Column Types in Analytical Data
measure_column_summary(): Get Column Summary by Type
measure_column_patterns: Common column naming patterns for analytical data
set_measure_roles(): Set Measure Roles in a Recipe
check_measure_recipe(): Check Measure Recipe Structure

Axis Validation

Axis semantics and data quality validation

validate_measure(): Validate measure data
measure_axis_info(): Get axis information from measure data
infer_axis_type(): Infer axis type from location values
check_axis_consistency(): Check axis consistency across samples
measure_quality_summary(): Summarize measure data quality

Validation Reporting

Generate reproducible analytical method validation reports

measure_validation_report(): Create an Analytical Method Validation Report
render_validation_report(): Render a Validation Report to Document Format
has_validation_section(): Check if validation report has a section
get_validation_section(): Get validation section data
add_validation_section(): Add or update a validation section
print(<measure_validation_report>): Print a Validation Report
summary(<measure_validation_report>): Summarize a Validation Report
tidy(<measure_validation_report>): Tidy a Validation Report

Visualization & Diagnostics

Plotting and diagnostic functions for measure data

autoplot(<measure_tbl>) autoplot(<measure_list>) autoplot(<recipe>): Autoplot Methods for Measure Objects
autoplot(<measure_bland_altman>): Plot Bland-Altman Analysis
autoplot(<measure_calibration>): Plot Calibration Curve Diagnostics
autoplot(<measure_control_chart>): Plot Control Chart
autoplot(<measure_deming_regression>) autoplot(<measure_passing_bablok>): Plot Method Comparison Regression
autoplot(<measure_linearity>): Plot Linearity Assessment Results
autoplot(<measure_matrix_effect>): Plot Matrix Effects
autoplot(<measure_proficiency_score>): Plot Proficiency Test Scores
autoplot(<measure_uncertainty_budget>): Plot Uncertainty Budget
fortify(<measure_tbl>) fortify(<measure_list>): Convert Measure Objects to Data Frames for Plotting
fortify(<measure_calibration>): Extract Calibration Curve Data
plot_measure_comparison(): Compare Multiple Preprocessing Recipes
measure_plot_summary(): Plot Summary Statistics for Measure Data