Background correction
Get x-axis values as text
Impute missing values with median
Kolmogorov-Smirnov tests on dataset
Flat pattern filter
Human Cachexia data
Apply by groups
Train models
Perform hierarchical clustering analysis
PCA importance
Get metadata value
Create dataset
indexes_to_xvalue_interval
Get the x-values of a vector of indexes
Fold change analysis
Low level fusion
Normalize samples
Multifactor ANOVA
plot_regression_coefs_pvalues
Plot regression coefficient and p-values
Get sample's names from DX files
Plot spectra (simple)
Plot spectra
Replace data value
Impute missing values with value replacement
Remove data variables
Read dataset from (J)DX files
Brazilian Propolis from different Harvest Seasons and different Agroecological Regions
Apply function to samples
remove_x_values_by_interval
Remove x-values by interval
Train and predict
Mean centering
Shift correction
Subset random samples
Correlation test of two variables or samples
Variables as metadata
PCA analysis (robust)
PCA biplot
Convert to hyperspec
MAIT_identify_metabolites
MAIT metabolite identification
Linear Regression
Replace metadata's value
Remove samples by NAs
Show cluster's members
Group peaks
Logarithmic transformation.
Impute missing values with linear approximation
Get number of x values
Read multiple CSVs
Savitzky-golay transformation
Baseline correction
count_missing_values_per_variable
Count missing values per variable
subset_x_values_by_interval
Subset x-values by interval
Offset correction
Kruskal-Wallis tests on dataset
Read data from (J)DX files
Linear regression r-squared
3D PCA scores plot
Remove interval of peaks
Boxplot of variables with metadata's variable factors
Get x-axis values as numbers
Perform k-means clustering analysis
Plot Kruskal-Wallis tests results
Multiplot
Convert from hyperspec
remove_samples_by_na_metadata
Remove samples by NA on metadata
Dataset correlations
Statistics of samples
2D PCA k-means plot
Plot dendrogram
Plot ANOVA results
Get data values
Linear regression p-values table
Plot t-tests results
Plot dendrogram
3D pca plot
Check dataset
Get sample names
Linear regression coefficient table
Remove variables by NAs
subset_by_samples_and_xvalues
Subset by samples and x-values
2D PCA scores plot
Plot Kolmogorov-Smirnov tests results
Impute missing values with mean
remove_metadata_variables
Remove metadata's variables
Get x-values indexes
Smoothing interpolation
Get number of samples
PCA pairs plot
remove_peaks_interval_sample_list
Remove interval of peaks (sample list)
Scale data matrix
Subset metadata
Peaks per sample
Values per peak
Subset samples
Volcano plot
Read CSVs from folder
absorbance_to_transmittance
Convert absorbance to transmittance
Boxplot of variables
Perform cluster analysis
PCA k-means pairs plot
Peaks per samples
Aggregate samples
Get sample's names from SPC files
compare_regions_by_sample
Compare regions by sample
Read metadata
Cubic root transformation
Apply function to variables
Set value label
Plot kmeans clusters
Plot variable distribution on two factors
Standard Normal Variate
Get type of data
subset_samples_by_metadata_values
Subset samples by metadata values
Check type of data
Set new metadata
Read data from SPC files
xvalue_interval_to_indexes
Get indexes of an interval of x-values
Dataset from peaks
Correlations heatmap
Get metadata
Summary of variables importance
Multi Class Summary
Perform selection by filter
Brazilian Propolis from different Harvest Seasons and different Agroecological Regions (sample list)
Missing values imputation
Impute missing values with KNN
Merge two datasets
Train classifier
transmittance_to_absorbance
Convert transmittance to absorbance
Read CSV data
Metadata as variables
PCA scree plot
Remove samples
Statistics of variables
Transform data
Set samples names
Convert metadata to factor
Apply by group
Get value label
Get data as data frame
Fold change applied on two variables
3D PCA scores plot (interactive)
Linear regression on one variable
recursive_feature_elimination
Perform recursive feature elimination
Normalize data
Multiplicative scatter correction
Dataset summary
3D PCA k-means plot (interactive)
Predict samples
Read dataset from SPC files
Scale dataset
Set new x-values
3D PCA biplot (interactive)
Get data
Set x-label
Brazilian Propolis from different Harvest Seasons and different Agroecological Regions (dataset)
Subset x-values
multifactor_aov_varexp_table
Multifactor ANOVA variability explained table
t-Tests on dataset
Cassava Postharvest Physiological Deterioration
Analysis of variance
Count missing values
Convert from ChemoSpec
Find equal samples
count_missing_values_per_sample
Count missing values per sample
Get peak values
First derivative
Get x-axis label
Correlations test
Merge data and metadata
Data correction
Perform feature selection
Get metadata variable
Get data value
PCA analysis (classical)
Values per peak
multifactor_aov_pvalues_table
Multifactor ANOVA p-values table
Plot fold change results
Read dataset from CSV
Remove data
Read MS spectra