Seq2seq time-feature analysis based on variational model, with a wide range of distributions available for the latent variable.

Calculates a comprehensive list of features from profile hidden Markov models (HMMs) of proteins. 
    Adapts and ports features for use with HMMs instead of Position Specific Scoring Matrices, in order to take 
    advantage of more accurate multiple sequence alignment by programs such as 'HHBlits' Remmert et al. (2012) 
    <DOI:10.1038/nmeth.1818> and 'HMMer' Eddy (2011) <DOI:10.1371/journal.pcbi.1002195>. Features calculated by 
    this package can be used for protein fold classification, protein structural class prediction, 
    sub-cellular localization and protein-protein interaction, among other tasks. Some examples of features 
    extracted are found in Song et al. (2018) <DOI:10.3390/app8010089>, Jin & Zhu (2021) <DOI:10.1155/2021/8629776>, 
    Lyons et al. (2015) <DOI:10.1109/tnb.2015.2457906> and Saini et al. (2015) <DOI:10.1016/j.jtbi.2015.05.030>.

An object oriented system using object-based, also
	called prototype-based, rather than class-based object oriented ideas.

Performs minimax linkage hierarchical clustering.  Every cluster
    has an associated prototype element that represents that cluster as
    described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering 
    with Prototypes via Minimax Linkage," The Journal of the American 
    Statistical Association, 106(495), 1075-1084.

Pure C++ implementations for reading and writing several common data 
    formats based on Google protocol-buffers. Currently supports 'rexp.proto' for 
    serialized R objects, 'geobuf.proto' for binary geojson, and 'mvt.proto' for 
    vector tiles. This package uses the auto-generated C++ code by protobuf-compiler, 
    hence the entire serialization is optimized at compile time. The 'RProtoBuf' 
    package on the other hand uses the protobuf runtime library to provide a general-
    purpose toolkit for reading and writing arbitrary protocol-buffer data in R.