fix?

R/rep_glm.R

@@ -1,12 +1,12 @@
 #' A repeated logistic regression function
 #'
-#' @description For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
+#' For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 #' @param meas Effect meassure. Input as c() of columnnames, use dput().
 #' @param vars variables in model. Input as c() of columnnames, use dput().
 #' @param str variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
 #' @param dta data frame to pull variables from.
-#' @keywords logistic regression
+#' @keywords logistic
 #' @export
 #' @examples
 #'   l<-50

R/rep_lm.R

@@ -6,7 +6,7 @@
 #' @param string variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
 #' @param data data frame to pull variables from.
-#' @keywords linear regression
+#' @keywords linear
 #' @export
 #' @examples
 #' rep_lm()

R/rep_olr.R

@@ -1,15 +1,15 @@
 #' A repeated ordinal logistic regression function
 #'
-#' @description For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
+#' For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 #' @param meas Effect meassure. Input as c() of columnnames, use dput().
 #' @param vars variables in model. Input as c() of columnnames, use dput().
 #' @param str variables to test. Input as c() of columnnames, use dput().
 #' @param ci flag to get results as OR with 95% confidence interval.
 #' @param dta data frame to pull variables from.
-#' @keywords olr ordinal logistic regression
+#' @keywords olr
 #' @export
 #' @examples
-#'   rep_olr()
+#' rep_olr()
 
 
 rep_olr<-function(meas,vars,string,ci=FALSE,data){

R/rep_reg_cie.R

@@ -7,7 +7,7 @@
 #' @param data data frame to pull variables from.
 #' @param logistic flag to set logistic (TRUE) or linear (FALSE,standard) analysis.
 #' @param cut cut value for gating if including or dropping the tested variable. As suggested bu S. Greenland (1989).
-#' @keywords change-in-estimate
+#' @keywords estimate-in-estimate
 #' @export
 #' @examples
 #' rep_reg_cie()

man/rep_glm.Rd

@@ -32,4 +32,3 @@ For bivariate analyses. The confint() function is rather slow, causing the whole
   rep_glm(meas="y",vars="v3",string=preds,ci=F,data=d)
 }
 \keyword{logistic}
-\keyword{regression}

man/rep_lm.Rd

@@ -24,4 +24,3 @@ For bivariate analyses, to determine which variables to include in adjusted mode
 rep_lm()
 }
 \keyword{linear}
-\keyword{regression}

man/rep_olr.Rd

@@ -21,9 +21,6 @@ rep_olr(meas, vars, string, ci = FALSE, data)
 For bivariate analyses. The confint() function is rather slow, causing the whole function to hang when including many predictors and calculating the ORs with CI.
 }
 \examples{
-  rep_olr()
+rep_olr()
 }
-\keyword{logistic}
 \keyword{olr}
-\keyword{ordinal}
-\keyword{regression}

man/rep_reg_cie.Rd

@@ -25,4 +25,4 @@ For bivariate analyses. From "Modeling and variable selection in epidemiologic a
 \examples{
 rep_reg_cie()
 }
-\keyword{change-in-estimate}
+\keyword{estimate-in-estimate}