Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.

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>.