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.

The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from 
   studies that used different time intervals. Different time intervals between measurement occasions impose problems for 
   meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common 
   fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or 
   intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different 
   time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 
   'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., 
   continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical 
   model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows 
   analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power
   (post hoc power, required sample sizes).  
   See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>.
   and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.