Bayesian inference on the (discrete) power spectrum of time series.

Build and use B-splines for interpolation and regression.
  In case of regression, equality constraints as well as monotonicity
  and/or positivity of B-spline weights can be imposed. Moreover, 
  knot positions can be on regular grid or be part of 
  optimized parameters too (in addition to the spline weights).
  For this end, 'bspline' is able to calculate
  Jacobian of basis vectors as function of knot positions. User is provided with 
  functions calculating spline values at arbitrary points. These 
  functions can be differentiated and integrated to obtain B-splines calculating 
  derivatives/integrals at any point. B-splines of this package can 
  simultaneously operate on a series of curves sharing the same set of 
  knots. 'bspline' is written with concern about computing 
  performance that's why the basis and Jacobian calculation is implemented in C++.
  The rest is implemented in R but without notable impact on computing speed.

Quantile regression with B-splines under shape constraints.
    The initial version with cubic splines is now augmented with splines
    of degree 1 to 4. Constraints for degrees 3 (monotone) and 4
    (monotone and convex) use the Karlin-Studden SOCP characterization
    for the sign of the polynomial, while other constraints applied
    at the knots are added as linear problems. The method for cubic
    splines is described in Abbes (2026) <doi:10.5281/zenodo.17427913>.
    Other formulations are simple consequences of the other given
    references. This R implementation is intended for demonstration
    and prototyping. All B-spline and polynomial functions have been
    rewritten for consistency. An equivalent Python package is available
    at <https://pypi.org/project/BsplineQuantRegpy/>.

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