Sliding windows are a convenient ways to clusterize data mapped on the genome. There are two important parameters of a sliding window: the size of the window and the size of the overlap.
By default, sliding windows count the number of reads in each window of each input file. However, you can merge any information which is contained in the tags. You can compute the average, sum, median, max or min of the tags for each window. For instance, every window can contain the average cluster size, if you merge clusters instead of reads.
The output file is a GFF3 file, where each element is a window. There is a special tag for each window, whose name is nbElements if you counted the number of transcripts per sliding window. However, if you performed a min (resp. max, sum, median, average) operation on the tags value of the transcripts, then the tag of the window will be minValue (resp. maxValue, sumValue, medValue, avgValue). You can also specify the name of your tag (which is actually advised: nbReadsInSample1 will always be more informative than nbElements).