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.

Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.

Provides computational tools for estimating inverse regions and 
  constructing the corresponding simultaneous outer and inner confidence regions. 
  Acceptable input includes both one-dimensional and two-dimensional data for linear, 
  logistic, functional, and spatial generalized least squares regression models. 
  Functions are also available for constructing simultaneous confidence bands (SCBs) 
  for these models. The definition of simultaneous confidence regions (SCRs) follows 
  Sommerfeld et al. (2018) <doi:10.1080/01621459.2017.1341838>. Methods for estimating 
  inverse regions, SCRs, and the nonparametric bootstrap are based on 
  Ren et al. (2024) <doi:10.1093/jrsssc/qlae027>. Methods for constructing SCBs 
  are described in Crainiceanu et al. (2024) <doi:10.1201/9781003278726> 
  and Telschow et al. (2022) <doi:10.1016/j.jspi.2021.05.008>.