This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)

- Fixed potential memory error from iterating over the beginning/end of a
`std::map`

`netinf()`

with`log-normal`

model didn't run because of an index error in the argument check for`params`

- Fixed memory allocation error caused by uninitialized comparison

`netinf_`

used ceiling on integer which caused error on Solaris

`netinf()`

`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 network- Number of edges can now be chosen using a
**Vuong style test**. If this procedure should be used, a p-value is chosen at which the inference of new edges stops. This value is specified via the new`p_value_cutoff`

argument to`netinf()`

. - This lead to the netinf output having a
**fourth column**now, containing the p-value for each edge. The p-value is also available if a fixed number of edges is chosen. - If no starting values are provided via the
`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 test- The log normal distribution is now available as a diffusion model. With this
comes a
**change in the arguments**for`netinf`

. 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`

). - The output from
`netinf()`

now contains information on the model, parameters and iterations as attributes. See the documentation for details. - The
`policies`

dataset has been updated with over 600 new policies from the SPID database (access via`data(policies)`

). - Inferred cascade trees can now be returned by setting
`trees = TRUE`

.

- New function
`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.

- Inference of very uninformative edges (large number of edges) could lead for the software to break. Fixed now
- In
`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.

- C++ code is now modularized and headers are properly documented

- We made changes to the internal data structures of the netinf function, so it is much faster and memory efficient now.
`netinf()`

now has a shiny progress bar!`as.cascade`

is now completely removed (see release note on version 1.1.0).- New convenience function to subset cascades by time (
`subset_cascade_time`

) and by cascade id (`subset_cascade`

).

- Long running functions (that call compiled code) can now be interrupted without crashing the R session.
`as_cascade_long()`

and`as_cascade_wide()`

handle date input correctly now.`as_cascade_wide()`

couldn't handle data input of class`data.table`

.

- Use of igraph now conditional compliant with Writing R Extensions 1.1.3.1
- Fixed version number displayed in startup message

- Data format (long or wide) of
`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`

.

- x and y axis labels in
`plot.cascade`

with option`label_nodes=FALSE`

were reversed

First release