**Data-Driven Identification of SVAR Models** (**svars**)

Implements data-driven identification methods for structural vector autoregressive (SVAR) models. Based on an existing VAR model object (provided by e.g. VAR() from the ‘vars’ package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>).

**Compare Output and Run Time** (**comparer**)

Makes comparisons quickly for different functions or code blocks performing the same task with the function mbc(). Can be used to compare model fits to the same data or see which function runs faster.

**Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables** (**wNNSel**)

New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.

**Generalized Farlie-Gumbel-Morgenstern Copula** (**GFGM.copula**)

Compute bivariate dependence measures and perform bivariate competing risks analysis under the generalized Farlie-Gumbel-Morgenstern (FGM) copula. See Shih and Emura (2016) <doi:10.1007/s00362-016-0865-5> and Shih and Emura (2017, in re-submission) for details.

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