| som.nn-package | Topological k-NN Classifier Based on Self-Organising Maps |
| dist.fun.bubble | Bubble distance functions for topological k-NN classifier |
| dist.fun.inverse | Inverse exponential distance functions for topological k-NN classifier |
| dist.fun.linear | Linear distance functions for topological k-NN classifier |
| dist.fun.tricubic | Tricubic distance functions for topological k-NN classifier |
| dist.torus | Torus distance matrix |
| initialize-method | Constructor of SOMnn Class |
| norm.linear | Linear normalisation |
| norm.softmax | Softmax normalisation |
| plot-method | Plot method for S4 class 'SOMnn' |
| predict-method | predict method for S4 class 'SOMnn' |
| round.probabilities | Advanced rounding of vectors |
| som.nn | Topological k-NN Classifier Based on Self-Organising Maps |
| som.nn.accuracy | Calculate accuracy measures |
| som.nn.all.accuracy | Calculate overall accuracy |
| som.nn.confusion | Calculate confusion matrix |
| som.nn.continue | Continue hexagonal som training |
| som.nn.export.kohonen | Export a som.nn model as object of type 'kohonen' |
| som.nn.export.som | Export a som.nn model as object of type 'SOM' |
| som.nn.multitrain | Multi-step hexagonal som training |
| som.nn.set | Set parameters for k-NN-like classifier in som.nn model |
| som.nn.train | Hexagonal som training |
| som.nn.validate | Predict class labels for a validation dataset |
| som.nn.visual | Mapping function for SOMnn |
| SOMnn | An S4 class to hold a model for the topological classifier som.nn |
| SOMnn-class | An S4 class to hold a model for the topological classifier som.nn |