netinf_ used ceiling on integer which caused error on Solarisnetinf()netinf() got another speed-up. After the first edge, the computation time for each edge is reduced by the factor number of nodes in the networkp_value_cutoff argument to netinf().params argument parameters are initialized by choosing the midpoint between the maximum possible parameter value and the minimum possible value. These values are derived using the closed form MLE of the respective parameter, derived from either the minimum possible diffusion times (assuming a diffusion ‘chain’, i.e. a -> b -> c -> ...) or the maximum possible diffusion times (assuming a diffusion ‘fan’, i.e. a -> b, a -> c, a -> d,...).n_edges can now specify either an absolute number of edges, or a p-value cutoff in the interval (0, 1) for the Vuong testnetinf. Instead of lambda, parameters are now specified with a vector (or scalar depending on distribution) params. For exponential and rayleigh distributions params is just the rate / alpha parameter. For the log-normal distribution params specifies mean and variance (in that order). See the netinf() documentation for details on specificaiton and parametrization (?netinf).netinf() now contains information on the model, parameters and iterations as attributes. See the documentation for details.policies dataset has been updated with over 600 new policies from the SPID database (access via data(policies)).trees = TRUE.drop_nodes() now allows to drop nodes from all cascades in a cascade object.simulate_cascades()simulate_cascades() now supports passing of additional (isolated in the diffusion network) nodes via the nodes argument.simulate_cascades() now also supports the log-normal distribution.simulate_cascades() with partial cascades provided, it was possible that nodes experienced an event earlier than the last event in the partial cascade. Now, the earliest event time is the last observed event time in the partial cascade.netinf() now has a shiny progress bar!as.cascade is now completely removed (see release note on version 1.1.0).subset_cascade_time) and by cascade id (subset_cascade).as_cascade_long() and as_cascade_wide() handle date input correctly now.as_cascade_wide() couldn’t handle data input of class data.table.as.cascade is not bound to the class of the data object anymore. In 1.0.0 wide format had to be a matrix and long format had to be a dataframe. This did not make much sense. as.cascade is now deprecated and replaced by two new functions as_cascade_long and as_cascade_wide.plot.cascade with option label_nodes=FALSE were reversedFirst release