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.

Models integrate environmental DNA (eDNA) detection data and traditional survey data to jointly estimate species catch rate (see package vignette: <https://ednajoint.netlify.app/>). Models can be used with count data via traditional survey methods (i.e., trapping, electrofishing, visual) and replicated eDNA detection/nondetection data via polymerase chain reaction (i.e., PCR or qPCR) from multiple survey locations. Estimated parameters include probability of a false positive eDNA detection, a site-level covariates that scale the sensitivity of eDNA surveys relative to traditional surveys, and gear scaling coefficients for traditional gear types. Models are implemented with a Bayesian framework (Markov chain Monte Carlo) using the 'Stan' probabilistic programming language.