Nothing
R/lookup_tables.R
In papaja: Prepare American Psychological Association Journal Articles with R Markdown
Defines functions
localize
lookup_adjust_names
Documented in
localize
lookup_adjust_names
#' Lookup Tables for Column Names and Variable Labels
#'
#' `apa_print()` converts many statistical output objects to output objects that
#' contain an `apa_results_table/data.frame` with standardized column names and
#' variable labels. For this purpose, it relies on the lookup tables provided
#' here.
#'
#' @rdname lookup_tables
# #' @examples
# #' papaja:::lookup_names[["mean.of.x"]]
# #' papaja:::lookup_labels[["mean.of.x"]]
#' @keywords internal
lookup_names <- c(
# nuisance parameters
"Sum.Sq" = "sumsq"
, "Sum.of.Sq" = "sumsq"
, "Error.SS" = "sumsq_err"
, "RSS" = "sumsq_err"
, "Mean.Sq" = "meansq"
, "logLik" = "loglik"
, "AIC" = "AIC"
, "BIC" = "BIC"
, "npar" = "n.parameters"
, "alternative" = "alternative"
# term
, "Effect" = "term"
, "Term" = "term"
, "Predictor" = "term"
, "model.term" = "term"
, "model" = "model"
# estimate
, "Estimate" = "estimate"
, "estimate" = "estimate"
, "mean.of.the.differences" = "estimate"
, "mean.difference" = "estimate"
, "cor" = "estimate"
, "rho" = "estimate"
, "tau" = "estimate"
, "mean.of.x" = "estimate"
, "mean" = "estimate"
, "X.pseudo.median" = "estimate"
, "difference.in.location" = "estimate"
, "difference.in.means" = "estimate"
, "difference.in.proportions" = "estimate"
, "coefficients" = "estimate"
, "delta" = "estimate"
, "proportion" = "estimate"
, "p" = "estimate"
# estimate from effectsize package
, "Eta2" = "estimate"
, "Eta2_partial" = "estimate"
, "Eta2_generalized" = "estimate"
, "Omega2" = "estimate"
, "Omega2_partial" = "estimate"
, "Omega2_generalized" = "estimate"
, "Epsilon2" = "estimate"
, "Epsilon2_partial" = "estimate"
, "Epsilon2_generalized" = "estimate"
# ----
, "conf.int" = "conf.int"
, "hd.int" = "conf.int"
, "stderr" = "std.error"
, "std.err" = "std.error"
, "Std.Error" = "std.error"
, "Std..Error" = "std.error"
, "sigma" = "std.error"
# multivariate.statistic
, "Pillai" = "multivariate.statistic"
, "Wilks" = "multivariate.statistic"
, "Roy" = "multivariate.statistic"
, "Hotelling.Lawley" = "multivariate.statistic"
# statistic
, "t" = "statistic"
, "t.value" = "statistic"
, "tstat" = "statistic"
, "statistic" = "statistic"
, "approx.F" = "statistic"
, "F.value" = "statistic"
, "F" = "statistic"
, "F.ratio" = "statistic"
, "LRT" = "statistic"
, "Chisq" = "statistic"
, "chisq" = "statistic"
, "X.squared" = "statistic"
, "V" = "statistic"
, "W" = "statistic"
, "S" = "statistic"
, "T" = "statistic"
, "z" = "statistic"
, "z.value" = "statistic"
, "bf" = "statistic"
, "bf10" = "statistic"
, "bf01" = "statistic"
, "logbf" = "statistic"
, "logbf10" = "statistic"
, "logbf01" = "statistic"
, "Bartlett.s.K.2" = "statistic"
, "Bartlett.s.K.squared" = "statistic"
# df, df.residual
, "multivariate.df1" = "multivariate.df"
, "multivariate.df2" = "multivariate.df.residual"
, "parameter" = "df"
, "df" = "df"
, "Df" = "df"
, "DF" = "df"
, "Chi.Df" = "df"
, "parameter1" = "df"
, "parameter2" = "df.residual"
, "df1" = "df"
, "df2" = "df.residual"
, "num.Df" = "df"
, "den.Df" = "df.residual"
, "NumDF" = "df"
, "DenDF" = "df.residual"
, "numDF" = "df"
, "denDF" = "df.residual"
, "num.df" = "df"
, "denom.df" = "df.residual"
, "den.df" = "df.residual"
, "Res.Df" = "df.residual"
# p.value
, "p.value" = "p.value"
, "Pr..Chisq." = "p.value"
, "Pr..F." = "p.value"
, "Pr..PB." = "p.value"
, "Pr...t.." = "p.value"
, "Pr...z.." = "p.value"
, "pvalues" = "p.value"
, "error" = "mcmc.error"
)
#' @rdname lookup_tables
#' @keywords internal
lookup_labels <- c(
# nuisance parameters
"Sum.Sq" = "$\\mathit{SS}$"
, "Sum.of.Sq" = "$\\mathit{SS}$"
, "RSS" = "$\\mathit{SS}_{\\mathrm{res}}$"
, "Mean.Sq" = "$\\mathit{MS}$"
, "logLik" = "$\\ln L$"
, "AIC" = "$\\mathit{AIC}$"
, "BIC" = "$\\mathit{BIC}$"
, "npar" = "$k$"
, "alternative" = "$\\mathcal{H}_1$"
# term
, "Effect" = "Effect"
, "Term" = "Term"
, "Predictor" = "Predictor"
, "model.term" = "Term"
, "model" = "Model"
, "contrast" = "Contrast"
# estimate
, "cor" = "$r$"
, "rho" = "$r_{\\mathrm{s}}$" # capital or small S???
, "tau" = "$\\uptau$"
, "mean.of.x" = "$M$"
, "mean" = "$M$"
, "X.pseudo.median" = "$\\mathit{Mdn}^*$"
, "mean.of.the.differences" = "$M_D$"
, "mean.difference" = "$M_D$"
, "difference.in.location" = "$\\Delta \\mathit{Mdn}$"
, "difference.in.means" = "$\\Delta M$"
, "difference.in.proportions" = "$\\Delta \\hat\\pi$"
, "proportion" = "$\\hat\\pi$"
, "p" = "$\\hat\\pi$"
, "delta" = "$\\delta$"
# estimate from effectsize package
, "Eta2" = "$\\hat{\\eta}^2$"
, "Eta2_partial" = "$\\hat{\\eta}^2_p$"
, "Eta2_generalized" = "$\\hat{\\eta}^2_G$"
, "Omega2" = "$\\hat{\\omega}^2$"
, "Omega2_partial" = "$\\hat{\\omega}^2_p$"
, "Omega2_generalized" = "$\\hat{\\omega}^2_G$"
, "Epsilon2" = "$\\hat{\\epsilon}^2$"
, "Epsilon2_partial" = "$\\hat{\\epsilon}^2_p$"
, "Epsilon2_generalized" = "$\\hat{\\epsilon}^2_G$"
# standard error
, "stderr" = "$\\mathit{SE}$"
, "std.err" = "$\\mathit{SE}$"
, "Std.Error" = "$\\mathit{SE}$"
, "Std..Error" = "$\\mathit{SE}$"
, "sigma" = "$\\mathit{SE}$"
# multivariate.statistic
, "Pillai" = "$V$"
, "Wilks" = "$\\Lambda$"
, "Hotelling.Lawley" = "$T$"
, "Roy" = "$\\theta$"
# statistic
, "t" = "$t$"
, "tstat" = "$t$"
, "t.value" = "$t$"
, "F.value" = "$F$"
, "F" = "$F$"
, "F.ratio" = "$F$"
, "approx.F" = "$F$"
, "LRT" = "$\\chi^2$"
, "chisq" = "$\\chi^2$"
, "Chisq" = "$\\chi^2$"
, "X.squared" = "$\\chi^2$"
, "W" = "$W$"
, "V" = "$V$"
, "S" = "$S$"
, "T" = "$T$"
, "z" = "$z$"
, "z.value" = "$z$"
, "bf" = "$\\mathrm{BF}$"
, "bf10" = "$\\mathrm{BF}_{\\textrm{10}}$"
, "bf01" = "$\\mathrm{BF}_{\\textrm{01}}$"
, "logbf" = "$\\log \\mathrm{BF}$"
, "logbf10" = "$\\log \\mathrm{BF}_{\\textrm{10}}$"
, "logbf01" = "$\\log \\mathrm{BF}_{\\textrm{01}}$"
, "Bartlett.s.K.2" = "$K^2$"
, "Bartlett.s.K.squared" = "$K^2$"
# df, df.residual
, "multivariate.df" = "$\\mathit{df}$"
, "multivariate.df.residual" = "$\\mathit{df}_{\\mathrm{res}}$"
, "parameter" = "$\\mathit{df}$"
, "df" = "$\\mathit{df}$"
, "Df" = "$\\mathit{df}$"
, "DF" = "$\\mathit{df}$"
, "Chi.Df" = "$\\mathit{df}$"
, "parameter1" = "$\\mathit{df}$"
, "parameter2" = "$\\mathit{df}_{\\mathrm{res}}$"
, "df1" = "$\\mathit{df}$"
, "df2" = "$\\mathit{df}_{\\mathrm{res}}$"
, "num.Df" = "$\\mathit{df}$"
, "den.Df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "NumDF" = "$\\mathit{df}$"
, "DenDF" = "$\\mathit{df}_{\\mathrm{res}}$"
, "numDF" = "$\\mathit{df}$"
, "denDF" = "$\\mathit{df}_{\\mathrm{res}}$"
, "num.df" = "$\\mathit{df}$"
, "denom.df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "den.df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "Res.Df" = "$\\mathit{df}_{\\mathrm{res}}$"
# p.value
, "p.value" = "$p$"
, "Pr...t.." = "$p$"
, "Pr...z.." = "$p$"
, "pvalues" = "$p$"
, "Pr..Chisq." = "$p$"
, "Pr..F." = "$p$"
, "Pr..PB." = "$p$"
, "adj.p.value" = "$p_\\mathrm{adj}$"
, "error" = "$\\pm\\%$"
)
#' Lookup Table for P Value/Confindence Interval Adjustment Names
#'
#' `apa_print()` converts many statistical output objects that include
#' inferential statistics adjusted for multiple comparisons. To make these
#' adjustments transparent the statistics get an index with the
#' corresponding name. This function returns the proper names for these indices.
#'
#' @rdname lookup_tables
# #' @examples
# #' papaja:::lookup_adjust_names("fdr")
#' @keywords internal
lookup_adjust_names <- function(x) {
switch(
x
, "holm" = c(p.value = "Holm" , conf.int = "Bonferroni")
, "hochberg" = c(p.value = "Hochberg" , conf.int = "Bonferroni")
, "hommel" = c(p.value = "Hommel" , conf.int = "Bonferroni")
, "bonferroni" = c(p.value = "Bonferroni" , conf.int = "Bonferroni")
, "BH" = c(p.value = "BH" , conf.int = "Bonferroni")
, "BY" = c(p.value = "BY" , conf.int = "Bonferroni")
, "fdr" = c(p.value = "FDR" , conf.int = "Bonferroni")
, "tukey" = c(p.value = "Tukey" , conf.int = "Tukey")
, "scheffe" = c(p.value = "Scheff\\'e" , conf.int = "Scheff\\'e")
, "sidak" = c(p.value = "Sidak" , conf.int = "Sidak")
, "dunnettx" = c(p.value = "Dunnett" , conf.int = "Dunnett")
, "mvt" = c(p.value = "MV \\mathit{t}" , conf.int = "MV \\mathit{t}")
, "adj"
)
}
#' Lookup Table for Generated Words and Phrases
#'
#' Some words and phrases used throughout a papaja manuscript are automatically
#' generated and need to vary when the locale of a document is changed. This
#' function returns the words and phrases by language.
#'
#' @param x Integer. Locale.
#' @keywords internal
localize <- function(x) {
switch(
x
, list( # Default
author_note = "Author note"
, abstract = "Abstract"
, keywords = "Keywords"
, word_count = "Word count"
, table = "Table"
, figure = "Figure"
, note = "Note"
, correspondence = "Correspondence concerning this article should be addressed to "
, email = "E-mail"
)
, german = list(
author_note = "Anmerkung des Autors"
, abstract = "Zusammenfassung"
, keywords = "Schl\u00fcsselw\u00f6rter"
, word_count = "Wortanzahl"
, table = "Tabelle"
, figure = "Abbildung"
, note = "Anmerkung"
, correspondence = "Schriftverkehr diesen Artikel betreffend sollte adressiert sein an "
, email = "E-Mail"
)
, dutch = list(
author_note = "Over de auteur"
, abstract = "Samenvatting"
, keywords = "Trefwoorden"
, word_count = "Aantal woorden"
, table = "Tabel"
, figure = "Figuur"
, note = "Opmerking"
, correspondence = "Correspondentie betreffende dit artikel wordt geadresseerd aan "
, email = "E-mail"
)
)
}
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Defines functions localize lookup_adjust_names
Documented in localize lookup_adjust_names
#' Lookup Tables for Column Names and Variable Labels
#'
#' `apa_print()` converts many statistical output objects to output objects that
#' contain an `apa_results_table/data.frame` with standardized column names and
#' variable labels. For this purpose, it relies on the lookup tables provided
#' here.
#'
#' @rdname lookup_tables
# #' @examples
# #' papaja:::lookup_names[["mean.of.x"]]
# #' papaja:::lookup_labels[["mean.of.x"]]
#' @keywords internal
lookup_names <- c(
# nuisance parameters
"Sum.Sq" = "sumsq"
, "Sum.of.Sq" = "sumsq"
, "Error.SS" = "sumsq_err"
, "RSS" = "sumsq_err"
, "Mean.Sq" = "meansq"
, "logLik" = "loglik"
, "AIC" = "AIC"
, "BIC" = "BIC"
, "npar" = "n.parameters"
, "alternative" = "alternative"
# term
, "Effect" = "term"
, "Term" = "term"
, "Predictor" = "term"
, "model.term" = "term"
, "model" = "model"
# estimate
, "Estimate" = "estimate"
, "estimate" = "estimate"
, "mean.of.the.differences" = "estimate"
, "mean.difference" = "estimate"
, "cor" = "estimate"
, "rho" = "estimate"
, "tau" = "estimate"
, "mean.of.x" = "estimate"
, "mean" = "estimate"
, "X.pseudo.median" = "estimate"
, "difference.in.location" = "estimate"
, "difference.in.means" = "estimate"
, "difference.in.proportions" = "estimate"
, "coefficients" = "estimate"
, "delta" = "estimate"
, "proportion" = "estimate"
, "p" = "estimate"
# estimate from effectsize package
, "Eta2" = "estimate"
, "Eta2_partial" = "estimate"
, "Eta2_generalized" = "estimate"
, "Omega2" = "estimate"
, "Omega2_partial" = "estimate"
, "Omega2_generalized" = "estimate"
, "Epsilon2" = "estimate"
, "Epsilon2_partial" = "estimate"
, "Epsilon2_generalized" = "estimate"
# ----
, "conf.int" = "conf.int"
, "hd.int" = "conf.int"
, "stderr" = "std.error"
, "std.err" = "std.error"
, "Std.Error" = "std.error"
, "Std..Error" = "std.error"
, "sigma" = "std.error"
# multivariate.statistic
, "Pillai" = "multivariate.statistic"
, "Wilks" = "multivariate.statistic"
, "Roy" = "multivariate.statistic"
, "Hotelling.Lawley" = "multivariate.statistic"
# statistic
, "t" = "statistic"
, "t.value" = "statistic"
, "tstat" = "statistic"
, "statistic" = "statistic"
, "approx.F" = "statistic"
, "F.value" = "statistic"
, "F" = "statistic"
, "F.ratio" = "statistic"
, "LRT" = "statistic"
, "Chisq" = "statistic"
, "chisq" = "statistic"
, "X.squared" = "statistic"
, "V" = "statistic"
, "W" = "statistic"
, "S" = "statistic"
, "T" = "statistic"
, "z" = "statistic"
, "z.value" = "statistic"
, "bf" = "statistic"
, "bf10" = "statistic"
, "bf01" = "statistic"
, "logbf" = "statistic"
, "logbf10" = "statistic"
, "logbf01" = "statistic"
, "Bartlett.s.K.2" = "statistic"
, "Bartlett.s.K.squared" = "statistic"
# df, df.residual
, "multivariate.df1" = "multivariate.df"
, "multivariate.df2" = "multivariate.df.residual"
, "parameter" = "df"
, "df" = "df"
, "Df" = "df"
, "DF" = "df"
, "Chi.Df" = "df"
, "parameter1" = "df"
, "parameter2" = "df.residual"
, "df1" = "df"
, "df2" = "df.residual"
, "num.Df" = "df"
, "den.Df" = "df.residual"
, "NumDF" = "df"
, "DenDF" = "df.residual"
, "numDF" = "df"
, "denDF" = "df.residual"
, "num.df" = "df"
, "denom.df" = "df.residual"
, "den.df" = "df.residual"
, "Res.Df" = "df.residual"
# p.value
, "p.value" = "p.value"
, "Pr..Chisq." = "p.value"
, "Pr..F." = "p.value"
, "Pr..PB." = "p.value"
, "Pr...t.." = "p.value"
, "Pr...z.." = "p.value"
, "pvalues" = "p.value"
, "error" = "mcmc.error"
)
#' @rdname lookup_tables
#' @keywords internal
lookup_labels <- c(
# nuisance parameters
"Sum.Sq" = "$\\mathit{SS}$"
, "Sum.of.Sq" = "$\\mathit{SS}$"
, "RSS" = "$\\mathit{SS}_{\\mathrm{res}}$"
, "Mean.Sq" = "$\\mathit{MS}$"
, "logLik" = "$\\ln L$"
, "AIC" = "$\\mathit{AIC}$"
, "BIC" = "$\\mathit{BIC}$"
, "npar" = "$k$"
, "alternative" = "$\\mathcal{H}_1$"
# term
, "Effect" = "Effect"
, "Term" = "Term"
, "Predictor" = "Predictor"
, "model.term" = "Term"
, "model" = "Model"
, "contrast" = "Contrast"
# estimate
, "cor" = "$r$"
, "rho" = "$r_{\\mathrm{s}}$" # capital or small S???
, "tau" = "$\\uptau$"
, "mean.of.x" = "$M$"
, "mean" = "$M$"
, "X.pseudo.median" = "$\\mathit{Mdn}^*$"
, "mean.of.the.differences" = "$M_D$"
, "mean.difference" = "$M_D$"
, "difference.in.location" = "$\\Delta \\mathit{Mdn}$"
, "difference.in.means" = "$\\Delta M$"
, "difference.in.proportions" = "$\\Delta \\hat\\pi$"
, "proportion" = "$\\hat\\pi$"
, "p" = "$\\hat\\pi$"
, "delta" = "$\\delta$"
# estimate from effectsize package
, "Eta2" = "$\\hat{\\eta}^2$"
, "Eta2_partial" = "$\\hat{\\eta}^2_p$"
, "Eta2_generalized" = "$\\hat{\\eta}^2_G$"
, "Omega2" = "$\\hat{\\omega}^2$"
, "Omega2_partial" = "$\\hat{\\omega}^2_p$"
, "Omega2_generalized" = "$\\hat{\\omega}^2_G$"
, "Epsilon2" = "$\\hat{\\epsilon}^2$"
, "Epsilon2_partial" = "$\\hat{\\epsilon}^2_p$"
, "Epsilon2_generalized" = "$\\hat{\\epsilon}^2_G$"
# standard error
, "stderr" = "$\\mathit{SE}$"
, "std.err" = "$\\mathit{SE}$"
, "Std.Error" = "$\\mathit{SE}$"
, "Std..Error" = "$\\mathit{SE}$"
, "sigma" = "$\\mathit{SE}$"
# multivariate.statistic
, "Pillai" = "$V$"
, "Wilks" = "$\\Lambda$"
, "Hotelling.Lawley" = "$T$"
, "Roy" = "$\\theta$"
# statistic
, "t" = "$t$"
, "tstat" = "$t$"
, "t.value" = "$t$"
, "F.value" = "$F$"
, "F" = "$F$"
, "F.ratio" = "$F$"
, "approx.F" = "$F$"
, "LRT" = "$\\chi^2$"
, "chisq" = "$\\chi^2$"
, "Chisq" = "$\\chi^2$"
, "X.squared" = "$\\chi^2$"
, "W" = "$W$"
, "V" = "$V$"
, "S" = "$S$"
, "T" = "$T$"
, "z" = "$z$"
, "z.value" = "$z$"
, "bf" = "$\\mathrm{BF}$"
, "bf10" = "$\\mathrm{BF}_{\\textrm{10}}$"
, "bf01" = "$\\mathrm{BF}_{\\textrm{01}}$"
, "logbf" = "$\\log \\mathrm{BF}$"
, "logbf10" = "$\\log \\mathrm{BF}_{\\textrm{10}}$"
, "logbf01" = "$\\log \\mathrm{BF}_{\\textrm{01}}$"
, "Bartlett.s.K.2" = "$K^2$"
, "Bartlett.s.K.squared" = "$K^2$"
# df, df.residual
, "multivariate.df" = "$\\mathit{df}$"
, "multivariate.df.residual" = "$\\mathit{df}_{\\mathrm{res}}$"
, "parameter" = "$\\mathit{df}$"
, "df" = "$\\mathit{df}$"
, "Df" = "$\\mathit{df}$"
, "DF" = "$\\mathit{df}$"
, "Chi.Df" = "$\\mathit{df}$"
, "parameter1" = "$\\mathit{df}$"
, "parameter2" = "$\\mathit{df}_{\\mathrm{res}}$"
, "df1" = "$\\mathit{df}$"
, "df2" = "$\\mathit{df}_{\\mathrm{res}}$"
, "num.Df" = "$\\mathit{df}$"
, "den.Df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "NumDF" = "$\\mathit{df}$"
, "DenDF" = "$\\mathit{df}_{\\mathrm{res}}$"
, "numDF" = "$\\mathit{df}$"
, "denDF" = "$\\mathit{df}_{\\mathrm{res}}$"
, "num.df" = "$\\mathit{df}$"
, "denom.df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "den.df" = "$\\mathit{df}_{\\mathrm{res}}$"
, "Res.Df" = "$\\mathit{df}_{\\mathrm{res}}$"
# p.value
, "p.value" = "$p$"
, "Pr...t.." = "$p$"
, "Pr...z.." = "$p$"
, "pvalues" = "$p$"
, "Pr..Chisq." = "$p$"
, "Pr..F." = "$p$"
, "Pr..PB." = "$p$"
, "adj.p.value" = "$p_\\mathrm{adj}$"
, "error" = "$\\pm\\%$"
)
#' Lookup Table for P Value/Confindence Interval Adjustment Names
#'
#' `apa_print()` converts many statistical output objects that include
#' inferential statistics adjusted for multiple comparisons. To make these
#' adjustments transparent the statistics get an index with the
#' corresponding name. This function returns the proper names for these indices.
#'
#' @rdname lookup_tables
# #' @examples
# #' papaja:::lookup_adjust_names("fdr")
#' @keywords internal
lookup_adjust_names <- function(x) {
switch(
x
, "holm" = c(p.value = "Holm" , conf.int = "Bonferroni")
, "hochberg" = c(p.value = "Hochberg" , conf.int = "Bonferroni")
, "hommel" = c(p.value = "Hommel" , conf.int = "Bonferroni")
, "bonferroni" = c(p.value = "Bonferroni" , conf.int = "Bonferroni")
, "BH" = c(p.value = "BH" , conf.int = "Bonferroni")
, "BY" = c(p.value = "BY" , conf.int = "Bonferroni")
, "fdr" = c(p.value = "FDR" , conf.int = "Bonferroni")
, "tukey" = c(p.value = "Tukey" , conf.int = "Tukey")
, "scheffe" = c(p.value = "Scheff\\'e" , conf.int = "Scheff\\'e")
, "sidak" = c(p.value = "Sidak" , conf.int = "Sidak")
, "dunnettx" = c(p.value = "Dunnett" , conf.int = "Dunnett")
, "mvt" = c(p.value = "MV \\mathit{t}" , conf.int = "MV \\mathit{t}")
, "adj"
)
}
#' Lookup Table for Generated Words and Phrases
#'
#' Some words and phrases used throughout a papaja manuscript are automatically
#' generated and need to vary when the locale of a document is changed. This
#' function returns the words and phrases by language.
#'
#' @param x Integer. Locale.
#' @keywords internal
localize <- function(x) {
switch(
x
, list( # Default
author_note = "Author note"
, abstract = "Abstract"
, keywords = "Keywords"
, word_count = "Word count"
, table = "Table"
, figure = "Figure"
, note = "Note"
, correspondence = "Correspondence concerning this article should be addressed to "
, email = "E-mail"
)
, german = list(
author_note = "Anmerkung des Autors"
, abstract = "Zusammenfassung"
, keywords = "Schl\u00fcsselw\u00f6rter"
, word_count = "Wortanzahl"
, table = "Tabelle"
, figure = "Abbildung"
, note = "Anmerkung"
, correspondence = "Schriftverkehr diesen Artikel betreffend sollte adressiert sein an "
, email = "E-Mail"
)
, dutch = list(
author_note = "Over de auteur"
, abstract = "Samenvatting"
, keywords = "Trefwoorden"
, word_count = "Aantal woorden"
, table = "Tabel"
, figure = "Figuur"
, note = "Opmerking"
, correspondence = "Correspondentie betreffende dit artikel wordt geadresseerd aan "
, email = "E-mail"
)
)
}
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