Function reference • specProc

Univariate filtering

Functions used to assess individual variables or features independently.

zscore(): Classical or Robust Z-Score

iqrMethod(): Interquartile Range Method

directOutlyingness(): Directional Outlyingness for Skewed Distribution

Multivariate filtering

orthogonalization methods

Functions used to identify and remove unwanted covariance structures and sources of variance from the multivariate data.

epo(): External Parameter Orthogonalization

osc(): Orthogonal Signal Correction

nas(): Net Analyte Signal

opls(): Orthogonal Projections to Latent Structures

o2pls(): Modified Orthogonal Projections to Latent Structures

direct_osc(): Direct Orthogonal Signal Correction

projected_osc(): Projected Orthogonal Signal Correction

yGradientglsw(): y-Gradient Generalized Least Squares Weighting

direct_orthogonal(): Direct Orthogonalization

calibration transfer

Functions used to to transfer a calibration model developed on one instrument to another instrument.

pds(): Piecewise Direct Standardization

glsw(): Generalized Least Squares Weighting

Peak fitting

Functions used to characterize spectral lines. The choice of the fitting function depends on the specific broadening mechanisms and physical conditions of the emitting environment.

peakfit(): Peak Fitting

multipeakfit(): Multiple Peaks Fitting

Data visualization

Functions that help assess data visually.

plotfit(): Plotting of Fitted Spectral Line

plotSpec(): Plotting of Spectra

outlierplot(): Univariate Representation of Multivariate Outliers

adjusted_boxplot(): Adjusted Boxplot

generalized_boxplot(): Generalized Boxplot

Smoothing

Functions used to increase the signal-to-noise ratio.

average(): Fast Average for Large Spectral Dataset

Baseline correction

Functions used to fit and remove the background emission.

whittaker(): Asymmetric Least Squares

baseline_lsp(): Least-Squares Polynomial

baseline_arpls(): Asymmetrically Reweighted Penalized Least Squares

Normalization

samples normalization

Functions used to adjust the systematic differences among samples.

msc(): Multiplicative Signal Correction

normalize(): Spectra Normalization

data scaling

Functions used to adjust each variable/feature by a scaling factor computed based on the dispersion of the variable.

snv(): Standard Normal Variate

pareto(): Pareto Scaling

minmax(): Min-Max Normalization

center(): Data Centering

poisson_scale(): Poisson Scaling

data transformation

Function used to transform variable(s) toward central normality.

robustBCYJ(): Robust Box-Cox and Yeo-Johnson Transformation

Robust statistical estimators

biweight

Functions that use Tukey’s biweight formalism.

biweight_scale(): Biweight Scale

biweight_location(): Biweight Location

biweight_midvariance(): Biweight Midvariance

biweight_midcovariance(): Biweight Midcovariance

biweight_midcorrelation(): Biweight Midcorrelation

tail weight

Functions used to measure the tail weight of a distribution.

quantile_weight(): Quantile Tail Weight Measure

medcouple_weight(): Medcouple Tail Weight Measure

scale

Function used to compute highly robust estimators for scale (or dispersion).

umad(): Unbiased Median Absolute Deviation

rousseeuwCroux(): Rousseeuw-Croux Scale Estimators

Special functions

Functions used for charcaterizing plasma emission in laser spectroscopy.

tukeyGH(): Tukey g-and-h Parametric Distribution

gaussian(): Gaussian Function

lorentzian(): Lorentzian Function

pseudo_voigt(): Pseudo-Voigt Function

Miscellaneous

correlation(): Correlation Coefficients: Pearson, Spearman, Kendall, Chatterjee, and Biweight Midcorrelation

summaryStats(): Classical or Robust Descriptive Statistics