Performs smoothed (and non-smoothed) principal/independent components analysis
  of functional data. Various functional pre-whitening approaches are implemented as 
  discussed in Vidal and Aguilera (2022) “Novel whitening approaches in functional 
  settings", <doi:10.1002/sta4.516>. Further whitening representations of functional 
  data can be derived in terms of a few principal components, providing an avenue 
  to explore hidden structures in low dimensional settings: see Vidal,
  Rosso and Aguilera (2021) “Bi-smoothed functional independent component 
  analysis for EEG artifact removal”, <doi:10.3390/math9111243>.

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to 
    Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>,
    Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>,
    Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>,
    Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>,
    Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and
    Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.

Implementation of commonly used penalized functional linear regression models, including the Smooth and Locally Sparse (SLoS) method by Lin et al. (2016) <doi:10.1080/10618600.2016.1195273>, Nested Group bridge Regression (NGR) method by Guan et al. (2020) <doi:10.1080/10618600.2020.1713797>, Functional Linear Regression That's interpretable (FLIRTI) by James et al. (2009) <doi:10.1214/08-AOS641>, and the Penalized B-spline regression method.

Builds and runs 'c++' code for classes that encapsulate state space model, particle filtering algorithm pairs.
    Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, 
    the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized 
    particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the 'c++' library 
    'pf', see Brown (2020) <doi:10.21105/joss.02599>.