Stochastic blockmodeling of one-mode and linked networks as implemented in Škulj and Žiberna (2022) <doi:10.1016/j.socnet.2022.02.001>. The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Collision Risk Models for avian fauna (seabird and migratory birds) at
    offshore wind farms. The base deterministic model is derived from 
    Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. 
    This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code 
    used here is heavily derived from this work with input from Dr A. Cook at the 
    British Trust for Ornithology. These collision risk models are useful for 
    marine ornithologists who are working in the offshore wind industry, particularly 
    in UK waters. However, many of the species included in the stochastic collision 
    risk models can also be found in the North Atlantic in the 
    United States and Canada, and could be applied there.

Implementations of stochastic, limited-memory quasi-Newton optimizers,
	similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
	for smooth stochastic optimization. Implements the following methods:
	oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
	SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
	adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
	Provides functions for easily creating R objects
	with partial_fit/predict methods from some given objective/gradient/predict functions.
	Includes an example stochastic logistic regression using these optimizers.
	Provides header files and registered C routines for using it directly from C/C++.