Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, <doi:10.1093/bioinformatics/btw753>.

Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

Collection of data sets from various assessments that can be used to 
    evaluate psychometric models. These data sets have been analyzed in the
    following papers that introduced new methodology as part of the application section:
    Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) <doi:10.1111/bmsp.12307>,
    Culpepper, S. A., & Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>,
    Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>,
    Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>,
    Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
    Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
    Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
    Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
    and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.

A series of R functions that come in handy while working with metabarcoding data.
  The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way.
  In a way it is all about putting together the grammar of the  'tidyverse' from Wickham et al.(2019) <doi:10.21105/joss.01686> 
  with the functions we have used in community ecology compiled in packages like 'vegan' from Dixon (2003) <doi:10.1111/j.1654-1103.2003.tb02228.x>
  and 'phyloseq' McMurdie & Holmes (2013) <doi:10.1371/journal.pone.0061217>.
  The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader' and 'fastq_reader'),
  to process 'cutadapt' Martin (2011) <doi:10.14806/ej.17.1.200> 'info-file' output. When it comes to sequence counts across samples, the package
  works with the long format in mind (a three column 'tibble' with Sample, Sequence and counts ), with functions to move from there to the wider format.