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Choose at least 2 samples to compare in the matrix.
Select the metric used to compare samples.
Choose a sample with multivariate data for the heatmap.
Configure the features and visualization type.
Choose at least 3 samples to compare using MDS.
Configure how the MDS analysis is performed.
Select which outputs to generate.
Choose a sample to calculate maximum depositional age.
Choose which MDA metrics to calculate.
Fine-tune how MDA metrics are calculated.
Select which outputs to generate.
Choose samples with multivariate data for tensor analysis.
Select which outputs to generate from the analysis.
Choose which samples to include in the distribution plot.
Configure the type of distribution plot.
Choose which samples to include in the histogram.
Configure the histogram appearance and binning.
Choose at least 2 source samples to model the mixture.
Configure how the mixture model is computed.
Select which outputs to generate.
Choose samples to view their empirical kernel density estimates.
Override the KDE bandwidth for individual variables. Leave a field blank to use the automatic bandwidth. Values are in each variable's own units (e.g. Ma for Age). Smaller bandwidths give less smoothing. The data range of each variable across the selected samples is shown for reference.
No variables found. Add multivariate data to the spreadsheet first.
Leave out grains whose value for a variable falls inside any of its ranges (inclusive). Excluded grains are removed from every variable, not just the one they were excluded by. To exclude by age, use Project Settings → Age Range.
Select which outputs to generate.
Choose samples to analyze for optimal tensor rank.
Configure the rank search parameters.
Override the KDE bandwidth for individual variables. Leave a field blank to use the automatic bandwidth. Values are in each variable's own units (e.g. Ma for Age). Smaller bandwidths give less smoothing. The data range of each variable across the selected samples is shown for reference.
No variables found. Add multivariate data to the spreadsheet first.
Leave out grains whose value for a variable falls inside any of its ranges (inclusive). Excluded grains are removed from every variable, not just the one they were excluded by. To exclude by age, use Project Settings → Age Range.
Select which analysis outputs to generate.
Choose samples for tensor factorization analysis.
Configure the tensor decomposition parameters.
Override the KDE bandwidth for individual variables. Leave a field blank to use the automatic bandwidth. Values are in each variable's own units (e.g. Ma for Age). Smaller bandwidths give less smoothing. The data range of each variable across the selected samples is shown for reference.
No variables found. Add multivariate data to the spreadsheet first.
Leave out grains whose value for a variable falls inside any of its ranges (inclusive). Excluded grains are removed from every variable, not just the one they were excluded by. To exclude by age, use Project Settings → Age Range.
Select which results to generate.