- Small fixes that arose from api changes in package
**ess**

The package has undergone a major make-over. A slight, but breakable,
change in the api of `fit_outlier`

. The documentation of
`fit_outlier`

has been updated and now includes more and
better examples of how and when to use the function. The
`fit_graph`

function is no longer a part of
**molic**. It now lives in its own package at ess and **molic**
is now dependend on **ess**. It is therefore now required
to run `include(ess)`

to have access to
`fit_graph`

.

The readme file has also undergone a major change - the former
example using `cars`

data has been removed; it was never
really a good example showing how to do outlier detection with
**molic**.

- A new data set,
`derma`

has been included and a new vignette using this data has been added. - The
`tgp_dat`

data has now been compressed to save disk space. - The
`plot.gengraph`

function applied to an object (`gengraph`

) returned from one of the graph fitting functions (`fit_graph`

,`fit_components`

etc.) now takes an input that let the user specify the color of the nodes.

`subgraph`

function is now provided.`sapply'`

s are now converted to`vapply'`

s for safety and potentially more speed when fitting graphs.

`pmf`

no longer plots the density of the deviances of a`outlier_model`

object. Use`plot`

for this instead; this is now consistent with the other related functions like`fit_outlier`

. Instead`pmf`

is used to construct the probability mass function of a decomposable graphical model which can be used to obtain probabilities of observing specific cells/observations/configurations.

**Development Model**

From this release we adopt the branching model introduced by Vincent Driessen

This means, that there are now two branches: the **master
branch** is always the current stable version, and the
**develop branch** is the develop version.

**New API**

- Functions like
`fit_outlier`

that depends on an adjacency list no accept`gengraph`

objects returned from`fit_graph`

- i.e. no need to use`adj_lst()`

first.

**New functions**

`generate_multiple_models`

- Given a class variable with \(1,2\ldots,
l\) levels and a new observation \(y\), this function is a convenient wrapper
around
`fit_graph`

and`fit_outlier`

that conducts all the hypothesis \(H_k:\) \(y\) has level \(k\) for \(k = 1,2,\ldots, l\).

- Given a class variable with \(1,2\ldots,
l\) levels and a new observation \(y\), this function is a convenient wrapper
around
`plot.multiple_models`

- Given an object returned from
`fit_multiple_models`

this function is used to visualize all the hypothesis tests for a single observation simultaneously. It is a`ggplot2`

object

- Given an object returned from
`plot.outlier`

- Given an object returned from
`fit_outlier`

this function is used to visualize the approximated density of the deviance under the null hypothesis. It is a`ggplot2`

object.

- Given an object returned from
`components`

- Return a list with all components of a graph

`fit_components`

**Misc** * All deviances are now non-negative as they
should be! Before, a constant was neglected which could potentially
confuse the users since a deviance is per definition non-negative.

- First release.