Analysis of minor alleles in Illumina sequencing data of viral
    genomes. Functions in 'vivaldi' primarily operate on vcf files.

A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.

Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.

Interactive visualization for Bayesian prior and posterior distributions. 
    This package facilitates an animated transition between prior and posterior distributions. 
    Additionally, it splits the distribution into bars based on the provided 'breaks,' displaying 
    the probability for each region. If no 'breaks' are provided, it defaults to zero.

Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.

Provides an interface to the VK API <https://vk.com/dev/methods>.
      VK <https://vk.com/> is the largest European online social networking
      service, based in Russia.

Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992> and Phillips et al. (2023) <doi:10.3897/BDJ.11.e96480>.

Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.

Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.