# Statistical Framework to Define Subgroups in Complex Datasets

## Documentation for package ‘Numero’ version 1.6.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

nroAggregate | Regional averages on a self-organizing map
nroCoalesce | Reduce collinearity within a dataset
nroCoalesce.merge | Reduce collinearity within a dataset
nroCoalesce.prune | Reduce collinearity within a dataset
nroCoalesce.split | Reduce collinearity within a dataset
nroColorize | Assign colors based on value
nroDestratify | Mitigate data stratification
nroImpute | Impute missing values
nroKmeans | K-means clustering
nroKohonen | Self-organizing map
nroLabel | Label pruning
nroMatch | Best-matching districts
nroPair | Match similar rows
nroPermute | Permutation analysis of map layout
nroPlot | Plot a self-organizing map
nroPlot.save | Plot a self-organizing map
nroPostprocess | Standardization using existing parameters
nroPreprocess | Data cleaning and standardization
nroRcppMatrix | Safety check for Rcpp calls
nroRcppVector | Safety check for Rcpp calls
nroStatistic | Estimate average value
nroSummary | Estimate subgroup statistics
nroTrain | Train self-organizing map
numero.clean | Clean datasets
numero.create | Create a self-organizing map
numero.evaluate | Self-organizing map statistics
numero.plot | Plot results from SOM analysis
numero.prepare | Prepare datasets for analysis
numero.quality | Self-organizing map statistics
numero.subgroup | Interactive subgroup assignment
numero.summary | Summarize subgroup statistics