Designed for the development and application of
hidden Markov models and profile HMMs for biological sequence analysis.
Contains functions for multiple and pairwise sequence alignment,
model construction and parameter optimization, file import/export,
implementation of the forward, backward and Viterbi algorithms for
conditional sequence probabilities, tree-based sequence weighting,
and sequence simulation.
Features a wide variety of potential applications including
database searching, gene-finding and annotation, phylogenetic
analysis and sequence classification.
Based on the models and algorithms described in Durbin et
al (1998, ISBN: 9780521629713).
graphics, openssl, kmer (>= 1.0.0), Rcpp (>= 0.12.5), stats

ape (>= 4.0), knitr, rmarkdown, testthat

Statistical Inference and Prediction of Annotations in
Phylogenetic Trees
c(person("George", "Vega Yon", role=c("aut","cre"),
email="g.vegayon@gmail.com", comment = c(ORCID = "0000-0002-3171-0844")),
person("National
Cancer Institute (NCI)", role = "fnd", comment = "Grant Number
5P01CA196569-02"),
person("USC Biostatistics", role = "cph")
)
Implements a parsimonious evolutionary model to analyze and
predict gene-functional annotations in phylogenetic trees as described in Vega
Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. With a focus on
computational efficiency, 'aphylo' makes it possible to estimate pooled
phylogenetic models, including thousands (hundreds) of annotations (trees) in
the same run. The package also provides the tools for visualization of
annotated phylogenies, calculation of posterior probabilities (prediction,)
and goodness-of-fit assessment featured in Vega Yon et al. (2021).
Rcpp (>= 0.12.1), Matrix, methods, coda, fmcmc, utils, MASS,
xml2
George Vega Yon [aut, cre] (<https://orcid.org/0000-0002-3171-0844>),
National Cancer Institute (NCI) [fnd] (Grant Number 5P01CA196569-02),
USC Biostatistics [cph]

Functions and Data Sets for the Book "A Progressive Introduction
to Linear Models"
Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).