@@ -25,7 +25,7 @@ Many numerical analyses are invalid when working with nominal data because the m

It uses bipartite graphs to show how two different types of data are linked together, and it puts them in the incidence matrix to continue with network analysis. Finding large submatrices with non-missing values and edge prediction are other applications of NIMAA to explore local and global similarities within the labels of nominal variables.

Then, using a variety of different network projection methods, two unipartite graphs are constructed on the given submatrix. NIMAA provides several options for clustering projected networks and selecting the best one based on internal measures and external prior knowledge (ground truth). When weighted bipartite networks are considered, the best clustering results are used as the benchmark for edge prediction analysis. This benchmark is used to figure out which imputation method is the best one to predict weight of edges in bipartite network. It looks at how similar the clustering results are before and after the imputations. By using edge prediciton analysis, we tried to get more information from the whole dataset even though there were some missing values.

Then, using a variety of different network projection methods, two unipartite graphs are constructed on the given submatrix. NIMAA provides several options for clustering projected networks and selecting the best one based on internal measures and external prior knowledge (ground truth). When weighted bipartite networks are considered, the best clustering results are used as the benchmark for edge prediction analysis. This benchmark is used to figure out which imputation method is the best one to predict weight of edges in bipartite network. It looks at how similar the clustering results are before and after the imputations. By using edge prediction analysis, we tried to get more information from the whole dataset even though there were some missing values.

```{r setup}

library(NIMAA)

```