bump version for CRAN

DESCRIPTION

@@ -1,12 +1,12 @@
 Package: cregg
 Type: Package
 Title: Simple Conjoint Tidying, Analysis, and Visualization
-Version: 0.2.4
-Date: 2018-07-23
+Version: 0.3.0
+Date: 2018-07-27
 Authors@R: c(person("Thomas J.", "Leeper", role = c("aut", "cre"),
                     email = "[email protected]",
                     comment = c(ORCID = "0000-0003-4097-6326")))
-Description: Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of AMCEs is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) <doi:10.1093/pan/mpt024>.
+Description: Simple tidying, analysis, and visualization of conjoint (factorial) experiments, including estimation and visualization of average marginal component effects ('AMCEs') and marginal means ('MMs') for weighted and un-weighted survey data, along with useful reference category diagnostics and statistical tests. Estimation of 'AMCEs' is based upon methods described by Hainmueller, Hopkins, and Yamamoto (2014) <doi:10.1093/pan/mpt024>.
 License: MIT + file LICENSE
 URL: https://github.com/leeper/cregg
 BugReports: https://github.com/leeper/cregg/issues
@@ -16,7 +16,7 @@ Imports:
     sandwich (>= 2.4-0),
     survey (>= 3.33),
     lmtest,
-    ggplot2,
+    ggplot2 (>= 2.0),
     ggstance,
     scales
 Suggests:

R/amce.R

@@ -28,7 +28,7 @@
 #' data("immigration")
 #' # estimating AMCEs with constraints
 #' amce(immigration, ChosenImmigrant ~ Gender + ReasonForApplication * CountryOfOrigin,
-#'      id = ~CaseID, constraints = list(~ReasonForApplication + CountryOfOrigin))
+#'      id = ~CaseID)
 #' 
 #' # balance testing example
 #' plot(amce(immigration[!is.na(immigration$ethnocentrism),],

R/cj_tidy.R

@@ -72,7 +72,10 @@
 #' 
 #' 
 #' # perform reshape
-#' str(long <- cj_tidy(wide_conjoint, profile_variables = list1, task_variables = list2, id = ~ respondent))
+#' str(long <- cj_tidy(wide_conjoint,
+#'                     profile_variables = list1,
+#'                     task_variables = list2,
+#'                     id = ~ respondent))
 #' stopifnot(nrow(long) == nrow(wide_conjoint)*4*2)
 #' 
 #' # recode outcome so it is coded sensibly

R/plot_cj_amce.R

@@ -55,9 +55,6 @@
 #' d2_1$reasons <- "1,2,3"
 #' d2_2$reasons <- "1,2"
 #' plot(rbind(d2_1, d2_2), group = "reasons")
-#' 
-#' # plot AMCEs
-#' plot(d2)
 #' }
 #' @seealso \code{\link{amce}}, \code{\link{mm}}
 #' @import ggplot2