#' articles
#' @description Data.frame with common articles
#' @seealso clean_strings
"articles"
#' corp_data1
#' @description Some made up data on the top 10 US companies in the Fortune 500. Mock-matched to corp_data2 in examples/match_template.R
"corp_data1"
#' corp_data2
#' @description Some made up data on the top 10 US companies in the Fortune 500. Mock-matched to corp_data1 in examples/match_template.R
"corp_data2"
#' corporate_words
#' @description Data.frame with common corporate abbreviations in column 1 and corresponding long names in column 2. Useful for cleaning company names for matching.
#' @seealso clean_strings
"corporate_words"
#' fund_words
#' @description Data.frame with abbreviations common in the names of financial (i.e. mutual) funds in column 1 and corresponding long names in column 2. Useful for cleaning fund names for matching.
#' @seealso clean_strings
"fund_words"
#' State_FIPS
#' @description Data.table with state FIPS codes and abbreviations.
"State_FIPS"
#' World_Bank_Codes
#' @description World Bank 3-Character Country Codes for 213 countries
"World_Bank_Codes"
#' sp_char_words
#' @description Common special characters and their replacements for string cleaning
"sp_char_words"
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