Standardize the pre-processing of genomic variants before
performing a bacterial genome-wide association study (bGWAS). prewas creates
a variant matrix (where each row is a variant, each column is a sample, and
the entries are presence - 1 - or absence - 0 - of the variant) that can be
used as input for bGWAS tools. When creating the binary variant matrix,
prewas can perform 3 pre-processing steps including: dealing with
multiallelic SNPs, (optional) dealing with SNPs in overlapping genes, and
choosing a reference allele. prewas can output matrices for use with both
SNP-based bGWAS and gene-based bGWAS. This method is described in Saund et
al. (2019) <doi:10.1101/2019.12.20.873158>. prewas can also provide gene
matrices for variants with specific SnpEff annotations (Cingolani et al.
2012).

Standardize the pre-processing of genomic variants before
performing a bacterial genome-wide association study (bGWAS). 'prewas'
creates a variant matrix (where each row is a variant, each column is a
sample, and the entries are presence - 1 - or absence - 0 - of the variant)
that can be used as input for bGWAS tools. When creating the binary variant
matrix, 'prewas' can perform 3 pre-processing steps including: dealing with
multiallelic SNPs, (optional) dealing with SNPs in overlapping genes, and
choosing a reference allele. 'prewas' can output matrices for use with both
SNP-based bGWAS and gene-based bGWAS. This method is described in Saund et
al. (2020) <doi:10.1099/mgen.0.000368>. 'prewas' can also provide
gene matrices for variants with specific annotations from the 'SnpEff'
software (Cingolani et al. 2012).

Regular Expressions for Prices and Currencies
Functions to aid in the analysis of price and currency data by expediting data preprocessing. This includes extraction of relevant data (e.g. from text fields), conversion into numeric class, cleaning, and standardisation as appropriate.

dplyr, gsubfn, stringr, purrr, jsonlite, stats, lubridate,
curl, stringi
Functions to aid in microeconomic analysis and handling of price and currency data. This includes extraction of relevant data (e.g. from World Bank API, and other sources), data cleaning/parsing, and standardisation.