fixes for tibble pkg; version bump

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: broom.mixed
 Type: Package
 Title: Tidying Methods for Mixed Models
-Version: 0.2.4.9000
+Version: 0.2.5
 Authors@R: c(
     person("Ben", "Bolker", email = "[email protected]",
         role = c("aut", "cre"),

R/brms_tidiers.R

@@ -209,7 +209,7 @@ tidy.brmsfit <- function(x, parameters = NA,
         }
       }
       res_list$ran_pars <-
-        data_frame(
+        dplyr::tibble(
           group = sapply(ss2, grpfun),
           term = sapply(ss2, termfun)
         )
@@ -220,7 +220,7 @@ tidy.brmsfit <- function(x, parameters = NA,
       vals <- stringr::str_match_all(rterms, "_(.+?)\\[(.+?),(.+?)\\]")
 
       res_list$ran_vals <-
-        data_frame(
+        dplyr::tibble(
           group = plyr::laply(vals, function (v) { v[[2]] }),
           term = plyr::laply(vals, function (v) { v[[4]] }),
           level = plyr::laply(vals, function (v) { v[[3]] })
@@ -240,7 +240,7 @@ tidy.brmsfit <- function(x, parameters = NA,
     ## prefixes already removed for ran_vals; don't remove for ran_pars
   } else {
     ## if !use_effects
-    out <- data_frame(term = names(samples))
+    out <- dplyr::tibble(term = names(samples))
   }
   pointfun <- if (robust) stats::median else base::mean
   stdfun <- if (robust) stats::mad else stats::sd
@@ -302,7 +302,7 @@ augment.brmsfit <- function(x, data = stats::model.frame(x), newdata = NULL,
   ## allow optional arguments to augment, e.g. pred.type,
   ## residual.type, re.form ...
   pred <- do.call(stats::predict, args)
-  ret <- dplyr::data_frame(.fitted = pred[, "Estimate"])
+  ret <- dplyr::tibble(.fitted = pred[, "Estimate"])
   if (se.fit) ret[[".se.fit"]] <- pred[, "Est.Error"]
   if (is.null(newdata)) {
     ret[[".resid"]] <- stats::residuals(x)[, "Estimate"]

R/glmmADMB_tidiers.R

@@ -168,7 +168,7 @@ tidy.glmmadmb <- function(x, effects = c("fixed", "ran_pars"),
     )
 
     estimate <- vv2[[rscale]]
-    ret <- data_frame(group = vv2$grp, term, estimate)
+    ret <- dplyr::tibble(group = vv2$grp, term, estimate)
 
 
     if (conf.int) {

R/glmmTMB_tidiers.R

@@ -222,7 +222,7 @@ tidy.glmmTMB <- function(x, effects = c("ran_pars", "fixed"),
           ranpar_names
        )
     } else {
-        ret <- dplyr::data_frame(component=character(0),
+        ret <- dplyr::tibble(component=character(0),
                       group=character(0),
                       term=character(0),
                       estimate=numeric(0))

R/lme4_tidiers.R

@@ -116,7 +116,7 @@ fix_ran_vals <- function(g) {
 #'   \item{p.value}{P-value computed from t-statistic (may be missing/NA)}
 #'
 #' @importFrom plyr ldply
-#' @importFrom dplyr mutate bind_rows data_frame bind_cols
+#' @importFrom dplyr mutate bind_rows bind_cols
 #' @importFrom tibble rownames_to_column
 #' @importFrom tidyr gather spread
 #' @importFrom purrr map

R/mcmc_tidiers.R

@@ -102,7 +102,7 @@ tidyMCMC <- function(x,
   m <- if (robust) colMeans(ss) else apply(ss, 2, median)
 
   stdfun <- if (robust) stats::mad else stats::sd
-  ret <- dplyr::data_frame(
+  ret <- dplyr::tibble(
     term = names(m),
     estimate = m,
     std.error = apply(ss, 2, stdfun)

R/nlme_tidiers.R

@@ -73,7 +73,7 @@
 #' @importFrom plyr ldply
 #' @importFrom nlme getVarCov intervals
 #' @import dplyr
-## @importFrom dplyr data_frame select full_join
+## @importFrom dplyr tibble select full_join
 #'
 #' @export
 tidy.lme <- function(x, effects = c("ran_pars", "fixed"),
@@ -152,7 +152,7 @@ tidy.lme <- function(x, effects = c("ran_pars", "fixed"),
           "nonlinear models"
         }
       )
-      ret <- dplyr::data_frame()
+      ret <- dplyr::tibble()
     } else {
       vc <- nlme::getVarCov(x)
       ran_prefix <- switch(rscale,
@@ -192,7 +192,7 @@ tidy.lme <- function(x, effects = c("ran_pars", "fixed"),
         diag(vals) <- sqrt(diag(vc))
         vals <- c(lwrtri(vals), sigma(x))
       }
-      ret <- dplyr::data_frame(
+      ret <- dplyr::tibble(
         effect = "ran_pars",
         group = grpnames,
         term = c(nmvec),
@@ -217,7 +217,7 @@ tidy.lme <- function(x, effects = c("ran_pars", "fixed"),
             sub(re, "_\\2.\\1", nm[corterms],
               perl = TRUE
             )
-          return(dplyr::data_frame(
+          return(dplyr::tibble(
             term = nm, conf.low = z[, "lower"],
             conf.high = z[, "upper"]
           ))

R/rstanarm_tidiers.R

@@ -251,7 +251,7 @@ glance_stan <- function(x, looic = FALSE, ..., type) {
         sim <- x$fit@sim
     }
 
-    ret <- dplyr::data_frame(algorithm = algo)
+    ret <- dplyr::tibble(algorithm = algo)
 
     if (algo != "optimizing") {
         pss <- sim$n_save

R/utilities.R

@@ -97,7 +97,7 @@ reorder_frame <- function(x, first_cols = c("effect", "group", "term", "estimate
 
 ## FIXME: store functions to run as a list of expressions,
 ##  allow user-specified 'skip' argument?
-finish_glance <- function(ret = dplyr::data_frame(), x) {
+finish_glance <- function(ret = dplyr::tibble(), x) {
   stopifnot(length(ret) == 0 || nrow(ret) == 1)
 
   ## catch NULL, numeric(0), error responses
@@ -108,7 +108,7 @@ finish_glance <- function(ret = dplyr::data_frame(), x) {
     return(tt)
   }
 
-  newvals <- dplyr::data_frame(
+  newvals <- dplyr::tibble(
     sigma = tfun(sigma(x)),
     logLik = tfun(as.numeric(stats::logLik(x))),
     AIC = tfun(stats::AIC(x)),

inst/NEWS.Rd

@@ -4,7 +4,7 @@
 \title{broom.mixed News}
 \encoding{UTF-8}
 
-\section{CHANGES IN VERSION 0.2.4.9000}{
+\section{CHANGES IN VERSION 0.2.5}{
   \subsection{NEW FEATURES}{
     \itemize{
       \item \code{lmer} tidier gets \code{ddf.method} (applies only to
@@ -13,6 +13,11 @@
       \item experimental \code{GLMMadaptive} tidiers
     }
   }
+  \subsection{OTHER CHANGES}{
+    \itemize{
+      \item fixes for updates in tibble package
+    }
+  }
 }
 \section{CHANGES IN VERSION 0.2.4}{
   \subsection{NEW FEATURES}{