Package: proximetricsR 0.6.4

Introduction | Setup | Workflow overview | Prepare spectral data | Define preprocessing recipe | Build calibration model | Example 1: Build a single model | Serialize the model for deployment | Example 2: Build multiple models at once testing different pre-processings | Serialize the multiple models for deployment | Export for ProxiScout | Device-specific considerations | References

Introduction | Build an application | Read the calibration data and prepare it | Merge multiple datasets | Resample to constant resolution | Activate/deactivate rows for modeling | Get the names of the response variables | Calibrating models using calibrate_models() | formulas: defining what is to be modeled | metadata_list: specify the property metadata | preprocess_recipes: create pre-processing recipes | method: how to fit the spectral models | control: Setting up the calibration parameters | Calibrate the spectral models | Overview of the best models found | Checking all the models tested | Predicting the properties in unseen samples | Writting down an application file | Other functionality | Calibrate single models with calibrate | Write model-related files (tsv, cal, prj and rtf)

Introduction | ProxiScout predictive application package | Overview | Deployment workflow | operations.json | Purpose | File structure | Operation Object | Example | Execution model | proximetricsR function reference | Spectra scale (ID: 37) | Get absorbance (ID: 29) | Average readings (ID: 7) | SNV (ID: 2) | Detrending (ID: 3) | Savitzky-Golay smoothing and differentiation (ID: 83) | Variable selection (ID: 17) | model_info.json | Contents

Overview | Background: NIR spectroscopy and applications | Application workflows for ProxiMate and ProxiScout | Scope of proximetricsR

Summary | Calibrate an application and write a nax | Read ProxiMate application files (.nax) | Recalibrate application | Just re-fit the models | Recalibrate based on new data

Overview | Key concepts | Setup | Preprocessing constructors | Resampling: prep_resample() | Smoothing: prep_smooth() | Standard Normal Variate: prep_snv() | Derivatives: prep_derivative() | Detrending: prep_detrend() | Reflectance/Absorbance conversion: prep_transform() | Wavelength trimming: prep_wav_trim() | Building preprocessing recipes | Device compatibility | Building recipes | Applying recipes with process() | Practical examples | Example 1: ProxiMate workflow | Example 2: ProxiScout workflow with detrending | Example 3: Minimal preprocessing | Example 4: Wavelength band selection | Best practices | Order matters | Device-aware development | Reproducibility | Parameter tuning | Summary

Introduction | Correlation vs. covariance | Partial Least Squares Regression algorithms | Standard PLSR algorithm | Modified PLSR algorithm | NIRWise PLUS-compatible PLSR implementation | Derived matrices and diagnostics | PLS predictions | Predictions using scores | Predictions using regression coefficients | Extended PLSR: the XLS algorithm | References

Introduction | Structure of the ProxiMate predictive applications | Calibration data file (.tsv) | Local data file (.tsv) | Calibration model files (.cal) | Project files (.prj) | Report files (.rtf) | Application metadata file (.nad) | Application file (.nax) | References