Package encapsulates standard expressions for distances, times, luminosities, and other quantities useful in observational cosmology, including molecular line observations.  Currently coded for a flat universe only.

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

The COSSO regularization method automatically
        estimates and selects important function components by a
        soft-thresholding penalty in the context of smoothing spline
        ANOVA models. Implemented models include mean regression,
        quantile regression, logistic regression and the Cox regression
        models.

Parameter estimation, one-step ahead forecast and new location
    prediction methods for spatio-temporal data.

Contains functions that can determine whether a time series
	is second-order stationary or not (and hence evidence for
	locally stationarity). Given two non-stationary series (i.e.
	locally stationary series) this package can then discover
	time-varying linear combinations that are second-order stationary.
	Cardinali, A. and Nason, G.P. (2013)
	<doi:10.18637/jss.v055.i01>.

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation.
	Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest.
	Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers
	that don't accept observation weights.