Provides topic modeling and visualization by interfacing with the
    'BERTopic' library for 'Python' via 'reticulate'. See Grootendorst (2022)
    <doi:10.48550/arXiv.2203.05794>.

Calculates several entropy metrics for spatial data 
  inspired by Boltzmann's entropy formula.
  It includes metrics introduced by Cushman for landscape mosaics 
  (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), 
  and landscape gradients and point patterns
  (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for 
  landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>);
  and by Gao et al. for landscape gradients
  (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

An implementation of best subset selection in generalized linear model and Cox proportional hazard model via the primal dual active set algorithm proposed by Wen, C., Zhang, A., Quan, S. and Wang, X. (2020) <doi:10.18637/jss.v094.i04>. The algorithm formulates coefficient parameters and residuals as primal and dual variables and utilizes efficient active set selection strategies based on the complementarity of the primal and dual variables.

Computations for Bessel function for complex, real and partly
  'mpfr' (arbitrary precision) numbers; notably interfacing TOMS 644;
  approximations for large arguments, experiments, etc.

Best subset glm using information criteria or cross-validation,
        carried by using 'leaps' algorithm (Furnival and Wilson, 1974) <doi:10.2307/1267601>
        or complete enumeration (Morgan and Tatar, 1972) <doi:10.1080/00401706.1972.10488918>.
        Implements PCR and PLS using AIC/BIC.
        Implements one-standard deviation rule for use with the 'caret' package.

Creates plots showing scored HR experiments and plots of distribution of means of ranks of HR score from bootstrapping.

An implementation of intervention effect estimation for DAGs (directed acyclic graphs) learned from binary or continuous data. First, parameters are estimated or sampled for the DAG and then interventions on each node (variable) are propagated through the network (do-calculus). Both exact computation (for continuous data or for binary data up to around 20 variables) and Monte Carlo schemes (for larger binary networks) are implemented.