Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in 'Praat' using either derived signals generated directly in R with 'wrassp' or imported derived signals from 'Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of  flexibility. Also provides options for embedding audio in figures and animating figures.

Distance-based parametric bootstrap tests for clustering with 
  spatial neighborhood information. Some distance measures, 
  Clustering of presence-absence, abundance and multilocus genetic data 
  for species delimitation, nearest neighbor 
  based noise detection. Genetic distances between communities.
  Tests whether various distance-based regressions
  are equal. Try package?prabclus for on overview.

Provides a large number of functions from numerical analysis and
    linear algebra, numerical optimization, differential equations,
    time series, plus some well-known special mathematical functions.
    Uses 'MATLAB' function names where appropriate to simplify porting.

Streamline the creation of 'Docker' images with 'R' packages and dependencies embedded. The 'pracpac' package provides a 'usethis'-like interface to creating Dockerfiles with dependencies managed by 'renv'. The 'pracpac' functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.

Sample size calculations for practical equivalence trial design with a time to event endpoint.

Consider a possibly nonlinear nonparametric regression
   with p regressors. We provide evaluations by 13 methods to rank
   regressors by their practical significance or importance using 
   various methods, including machine learning tools. Comprehensive
   methods are as follows. 
   m6=Generalized partial correlation coefficient or
   GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
   Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
   m7= a generalization of psychologists' effect size incorporating 
   nonlinearity and many variables.
   m8= local linear partial (dy/dxi) using the 'np' package for kernel 
   regressions.
   m9= partial (dy/dxi) using the 'NNS' package.
   m10= importance measure using the 'NNS' boost function.
   m11= Shapley Value measure of importance (cooperative game theory).
   m12 and m13= two versions of the random forest algorithm.
   Taraldsen's exact density for sampling distribution of correlations added.