View source: R/structure_learning_dmmhc.R
dmmhc | R Documentation |
Learns a gaussian dynamic Bayesian network from a dataset. It allows the creation of markovian n nets rather than only markov 1.
dmmhc(
dt,
size = 2,
f_dt = NULL,
blacklist = NULL,
intra = TRUE,
blacklist_tr = NULL,
whitelist = NULL,
whitelist_tr = NULL,
...
)
dt |
the data.frame or data.table to be used |
size |
number of time slices of the net. Markovian 1 would be size 2 |
f_dt |
previously folded dataset, in case some specific rows have to be removed after the folding |
blacklist |
an optional matrix indicating forbidden arcs between nodes |
intra |
if TRUE, the intra-slice arcs of the network will be learnt. If FALSE, they will be ignored |
blacklist_tr |
an optional matrix indicating forbidden intra-slice arcs between nodes |
whitelist |
an optional matrix indicating obligatory arcs between nodes |
whitelist_tr |
an optional matrix indicating obligatory intra-slice arcs between nodes |
... |
additional parameters for |
the structure of the net
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.