Inference for a Generalised SBM with a Split Merge Sampler
person("Matthew", "Ludkin", email = "m.ludkin1@lancaster.ac.uk", role = c("aut", "cre", "cph"))
Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

person("Finnbar", "Lee", , "lee.finnbar@gmail.com", role = c("aut", "cre"))
Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.

Detecting structural breaks using rectangle covering
(non-parametric method).
c(person("Paul", "Fischer", role = c("aut", "cre","cph"),
email = "pafi@dtu.dk"),person("Astrid", "Hilbert", role = c("ctb","cph"),
email = "astrid.hilbert@lnu.se"))
Paul Fischer [aut, cre, cph],
Astrid Hilbert [ctb, cph]
The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.