Performs simulation and inference of diffusion processes on circle. Stochastic correlation models based on circular diffusion models are provided. For details see Majumdar, S. and Laha, A.K. (2024) "Diffusion on the circle and a stochastic correlation model"  <doi:10.48550/arXiv.2412.06343>.

Implementation of advanced stochastic counting processes. 
    Main models include the Fractional Counting Process at Levy times 
    (Garg et al. (2025) <doi:10.1007/s10955-025-03515-9>), the Generalized 
    Iterated Poisson Process (Soni & Pathak (2024) <doi:10.1007/s10959-024-01362-0>), 
    the Generalized Fractional Risk Process (Soni & Pathak (2024) <doi:10.1007/s11009-024-10111-z>), 
    and the Tempered Space-Time Fractional Negative Binomial Process 
    (Garg et al. (2025) <doi:10.1007/s11009-025-10179-1>).

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++.