Implements a multiple testing approach to the
    choice of a threshold gamma on the p-values using the
    Average Power Function (APF) and Bayes False Discovery
    Rate (FDR) robust estimation. Function apf_fdr() 
    estimates both quantities from either raw data or
    p-values. Function apf_plot() produces smooth graphs 
    and tables of the relevant results. Details of the methods
    can be found in Quatto P, Margaritella N, et al. (2019) 
    <doi:10.1177/0962280219844288>.

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).

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>. Focusing 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).

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/>).