Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).

Set of functions for Stochastic Data Envelopment Analysis. Chance constrained versions of radial, directional and additive DEA models are implemented, as long as super-efficiency models. See: Cooper, W.W.; Deng, H.; Huang, Z.; Li, S.X. (2002). <doi:10.1057/palgrave.jors.2601433>, Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) <doi:10.1016/j.orp.2024.100307>.

Implementation of statistical methods for the estimation of
    toroidal diffusions. Several diffusive models are provided, most of them
    belonging to the Langevin family of diffusions on the torus. Specifically,
    the wrapped normal and von Mises processes are included, which can be seen
    as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
    methods for approximate maximum likelihood estimation, organized in four
    blocks, is given: (i) based on the exact transition probability density,
    obtained as the numerical solution to the Fokker-Plank equation; (ii) based
    on wrapped pseudo-likelihoods; (iii) based on specific analytic
    approximations by wrapped processes; (iv) based on maximum likelihood of
    the stationary densities. The package allows the replicability of the
    results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.

Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), <doi:10.48550/arXiv.2602.10774>.