Bayesian estimation of inverse variance weighted (IVW),
    Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden
    et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for
    Mendelian randomization analyses.

A collection of functions for processing and analyzing metabolite data. 
    The namesake function mrbin() converts 1D 
    or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and
    performs basic processing steps in a reproducible way. Negative values, a
    common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used
    parameters are stored in a readable text file and can be restored from that
    file to enable exact reproduction of the data at a later time. The function fia() ranks features according
    to their impact on classifier models, especially artificial neural network models.

A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011> and extended in Flury, Gerber, Schmid and Furrer (2021) <DOI:10.1016/j.spasta.2020.100483>.

Compute bootstrap confidence intervals for the adjusted Schnabel and Schumacher-Eschmeyer multi-visit mark-recapture estimators based on Dettloff (2023) <doi:10.1016/j.fishres.2023.106756>.

Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.