| acore | Extraction of alpha cores for soft clusters |
| cselection | Repeated soft clustering for detection of empty clusters for estimation of optimised number of clusters |
| Dmin | Calculation of minimum centroid distance for a range of cluster numbers for estimation of optimised number of clusters |
| fill.NA | Replacement of missing values |
| filter.NA | Filtering of genes based on number of non-available expression values. |
| filter.std | Filtering of genes based on their standard deviation. |
| kmeans2 | K-means clustering for gene expression data |
| kmeans2.plot | Plotting results for k-means clustering |
| membership | Calculating of membership values for new data based on existing clustering |
| mestimate | Estimate for optimal fuzzifier m |
| mfuzz | Function for soft clustering based on fuzzy c-means. |
| mfuzz.plot | Plotting results for soft clustering |
| mfuzz.plot2 | Plotting results for soft clustering with additional options |
| mfuzzColorBar | Plots a colour bar |
| Mfuzzgui | Graphical user interface for Mfuzz package |
| overlap | Calculation of the overlap of soft clusters |
| overlap.plot | Visualisation of cluster overlap and global clustering structure |
| partcoef | Calculation of the partition coefficient matrix for soft clustering |
| randomise | Randomisation of data |
| standardise | Standardization of expression data for clustering. |
| standardise2 | Standardization in regards to selected time-point |
| table2eset | Conversion of table to Expression set object. |
| top.count | Determines the number for which each gene has highest membership value in all cluster |
| yeast | Gene expression data of the yeast cell cycle |
| yeast.table | Gene expression data of the yeast cell cycle as table |
| yeast.table2 | Gene expression data of the yeast cell cycle as table |