scorematchingad - Score Matching Estimation by Automatic Differentiation
Hyvärinen's score matching (Hyvärinen, 2005)
<https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful
estimation technique when the normalising constant for a
probability distribution is difficult to compute. This package
implements score matching estimators using automatic
differentiation in the 'CppAD' library
<https://github.com/coin-or/CppAD> and is designed for quickly
implementing score matching estimators for new models. Also
available is general robustification (Windham, 1995)
<https://www.jstor.org/stable/2346159>. Already in the package
are estimators for directional distributions (Mardia, Kent and
Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible
Polynomially-Tilted Pairwise Interaction model for
compositional data. The latter estimators perform well when
there are zeros in the compositions (Scealy and Wood, 2023)
<doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy,
Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>.
A partial interface to CppAD's ADFun objects is also available.