A graph based regression model from flat unstructured dataset. Each line in the input data set is treated as a node from which an edge to another line (node) can be formed. In the training process, a model is created which contains sparse graph adjacency matrix. This model is then used for prediction by taking a predictor and the model as inputs and outputs a prediction which is an average of the most similar node and its neighbours in the model graph.
| Version: | 0.2 |
| Depends: | R (≥ 3.2.4) |
| Imports: | stringr, igraph, KRLS, caTools, mclust, caret, stats, graphics |
| Published: | 2016-07-27 |
| Author: | Yossi Keshet |
| Maintainer: | Yossi Keshet <jossiekat at icloud.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | kinn results |
| Reference manual: | kinn.pdf |
| Package source: | kinn_0.2.tar.gz |
| Windows binaries: | r-devel: kinn_0.2.zip, r-release: kinn_0.2.zip, r-oldrel: kinn_0.2.zip |
| OS X Mavericks binaries: | r-release: kinn_0.2.tgz, r-oldrel: kinn_0.2.tgz |
| Old sources: | kinn archive |
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