Providing font files that can be used by the 'showtext' package.

Functions for converting and processing network data from a
        'SpatialLinesDataFrame' -Class object to an 'igraph'-Class object.

The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.

Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully 
    Bayesian estimation of dynamic survival models with shrinkage priors. 
    Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, 
    Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
    Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023)  <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.