Three global value chain (GVC) decompositions are implemented. 
    The Leontief decomposition derives the value added origin of exports by 
    country and industry as in Hummels, Ishii and Yi (2001). The Koopman, 
    Wang and Wei (2014) decomposition splits country-level exports into 9 
    value added components, and the Wang, Wei and Zhu (2013) decomposition 
    splits bilateral exports into 16 value added components. Various GVC 
    indicators based on these decompositions are computed in the 
    complimentary 'gvc' package. 
    --- References: ---
    Hummels, D., Ishii, J., & Yi, K. M. (2001). The nature and growth of 
       vertical specialization in world trade. Journal of international 
       Economics, 54(1), 75-96.
    Koopman, R., Wang, Z., & Wei, S. J. (2014). Tracing value-added and double 
       counting in gross exports. American Economic Review, 104(2), 459-94.
    Wang, Z., Wei, S. J., & Zhu, K. (2013). Quantifying international production 
       sharing at the bilateral and sector levels (No. w19677). 
       National Bureau of Economic Research.

A collection of functions to deal
        with nonparametric measurement error problems using
        deconvolution kernel methods. We focus two measurement error
        models in the package: (1) an additive measurement error model,
        where the goal is to estimate the density or distribution
        function from contaminated data; (2) nonparametric regression
        model with errors-in-variables. The R functions allow the
        measurement errors to be either homoscedastic or
        heteroscedastic. To make the deconvolution estimators
        computationally more efficient in R, we adapt the "Fast Fourier
        Transform" (FFT) algorithm for density estimation with
        error-free data to the deconvolution kernel estimation. Several
        methods for the selection of the data-driven smoothing
        parameter are also provided in the package. See details in:
        Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
        measurement error models: The R package decon. Journal of
        Statistical Software, 39(10), 1-24.