Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm. A paper describing all these methods will be submitted soon. The reference will be added to this file as soon as available.

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>).