Paternal recombination rate and maternal linkage disequilibrium 
    (LD) are estimated for pairs of biallelic markers such as single nucleotide 
    polymorphisms (SNPs) from progeny genotypes and sire haplotypes. The 
    implementation relies on paternal half-sib families. If maternal half-sib 
    families are used, the roles of sire/dam are swapped. Multiple families can 
    be considered. For parameter estimation, at least one sire has to be double 
    heterozygous at the investigated pairs of SNPs.
    Based on recombination rates, genetic distances between markers can be 
    estimated. Markers with unusually large recombination rate to markers in 
    close proximity (i.e. putatively misplaced markers) shall be discarded in 
    this derivation.
    A workflow description is attached as vignette.
    *A pipeline is available at GitHub*
    <https://github.com/wittenburg/hsrecombi>
    Hampel, Teuscher, Gomez-Raya, Doschoris, Wittenburg (2018) "Estimation of 
    recombination rate and maternal linkage disequilibrium in half-sibs" 
    <doi:10.3389/fgene.2018.00186>.
    Gomez-Raya (2012) "Maximum likelihood estimation of linkage disequilibrium 
    in half-sib families" <doi:10.1534/genetics.111.137521>.

Linear and logistic regression models penalized with hierarchical
  shrinkage priors for selection of biomarkers (or more general variable
  selection), which can be fitted using Stan (Carpenter et al. (2017)
  <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized
  horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>),
  as well as the projection predictive selection approach to recover a sparse
  set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020)
  <doi:10.1214/20-EJS1711>).

Fast, model-agnostic implementation of different H-statistics
    introduced by Jerome H. Friedman and Bogdan E. Popescu (2008)
    <doi:10.1214/07-AOAS148>.  These statistics quantify interaction
    strength per feature, feature pair, and feature triple.  The package
    supports multi-output predictions and can account for case weights.
    In addition, several variants of the original statistics are provided.
    The shape of the interactions can be explored through partial
    dependence plots or individual conditional expectation plots. 'DALEX'
    explainers, meta learners ('mlr3', 'tidymodels', 'caret') and most
    other models work out-of-the-box.