Running examples in 'simTool-Ex.R' failed
The error most likely occurred in:

> ### Name: eval_tibbles
> ### Title: Workhorse for simulation studies
> ### Aliases: eval_tibbles
> 
> ### ** Examples
> 
> rng = function(data, ...) {
+ ret = range(data)
+ names(ret) = c("min", "max")
+ ret
+ }
> 
> dg <- expand_tibble(fun = "rnorm", n = c(5L, 10L))
> pg <- expand_tibble(proc = c("rng", "median", "length"))
> 
> eval_tibbles(dg, pg,rep = 2, simplify=FALSE)
Warning in eval_tibbles(dg, pg, rep = 2, simplify = FALSE) :
  NAs introduced by coercion to integer range
# A tibble: 12 x 5
   fun       n replications proc   results  
   <chr> <int>        <int> <chr>  <list>   
 1 rnorm     5            1 rng    <dbl [2]>
 2 rnorm     5            1 median <dbl [1]>
 3 rnorm     5            1 length <int [1]>
 4 rnorm     5            2 rng    <dbl [2]>
 5 rnorm     5            2 median <dbl [1]>
 6 rnorm     5            2 length <int [1]>
 7 rnorm    10            1 rng    <dbl [2]>
 8 rnorm    10            1 median <dbl [1]>
 9 rnorm    10            1 length <int [1]>
10 rnorm    10            2 rng    <dbl [2]>
11 rnorm    10            2 median <dbl [1]>
12 rnorm    10            2 length <int [1]>
Number of data generating functions: 2
Number of analyzing procedures: 3
Number of replications: 2
Estimated replications per hour: NA
Start of the simulation: 2019-02-02 23:35:26
End of the simulation: 2019-02-02 23:35:26
> eval_tibbles(dg, pg,rep = 2)
Warning in eval_tibbles(dg, pg, rep = 2) :
  NAs introduced by coercion to integer range
# A tibble: 16 x 5
   fun       n replications proc    results
   <chr> <int>        <int> <chr>     <dbl>
 1 rnorm     5            1 rng     -1.38  
 2 rnorm     5            1 rng      1.36  
 3 rnorm     5            1 median  -0.0538
 4 rnorm     5            1 length   5     
 5 rnorm     5            2 rng     -0.415 
 6 rnorm     5            2 rng      1.10  
 7 rnorm     5            2 median  -0.0593
 8 rnorm     5            2 length   5     
 9 rnorm    10            1 rng     -0.707 
10 rnorm    10            1 rng      0.881 
11 rnorm    10            1 median   0.126 
12 rnorm    10            1 length  10     
13 rnorm    10            2 rng     -1.13  
14 rnorm    10            2 rng      1.98  
15 rnorm    10            2 median   0.103 
16 rnorm    10            2 length  10     
Number of data generating functions: 2
Number of analyzing procedures: 3
Number of replications: 2
Estimated replications per hour: NA
Start of the simulation: 2019-02-02 23:35:26
End of the simulation: 2019-02-02 23:35:26
> eval_tibbles(dg, pg,rep = 2, post_analyze = purrr::compose(tibble::as_tibble, t))
# A tibble: 12 x 5
   fun       n replications proc   results      
   <chr> <int>        <int> <chr>  <list>       
 1 rnorm     5            1 rng    <dbl [1 x 2]>
 2 rnorm     5            1 median <dbl [1 x 1]>
 3 rnorm     5            1 length <int [1 x 1]>
 4 rnorm     5            2 rng    <dbl [1 x 2]>
 5 rnorm     5            2 median <dbl [1 x 1]>
 6 rnorm     5            2 length <int [1 x 1]>
 7 rnorm    10            1 rng    <dbl [1 x 2]>
 8 rnorm    10            1 median <dbl [1 x 1]>
 9 rnorm    10            1 length <int [1 x 1]>
10 rnorm    10            2 rng    <dbl [1 x 2]>
11 rnorm    10            2 median <dbl [1 x 1]>
12 rnorm    10            2 length <int [1 x 1]>
Number of data generating functions: 2
Number of analyzing procedures: 3
Number of replications: 2
Estimated replications per hour: 461539
Start of the simulation: 2019-02-02 23:35:27
End of the simulation: 2019-02-02 23:35:27
> eval_tibbles(dg, pg,rep = 2, summary_fun = list(mean = mean, sd = sd))
# A tibble: 12 x 8
   fun       n replications summary_fun proc       min    max    value
   <chr> <int>        <int> <chr>       <chr>    <dbl>  <dbl>    <dbl>
 1 rnorm     5            1 mean        rng     -0.910  1.07   NA     
 2 rnorm     5            1 mean        median  NA     NA       0.294 
 3 rnorm     5            1 mean        length  NA     NA       5     
 4 rnorm     5            1 sd          rng      0.519  0.727  NA     
 5 rnorm     5            1 sd          median  NA     NA       1.23  
 6 rnorm     5            1 sd          length  NA     NA       0     
 7 rnorm    10            1 mean        rng     -0.781  1.60   NA     
 8 rnorm    10            1 mean        median  NA     NA      -0.0142
 9 rnorm    10            1 mean        length  NA     NA      10     
10 rnorm    10            1 sd          rng      0.184  0.237  NA     
11 rnorm    10            1 sd          median  NA     NA       0.404 
12 rnorm    10            1 sd          length  NA     NA       0     
Number of data generating functions: 2
Number of analyzing procedures: 3
Number of replications: 2
Estimated replications per hour: 83720
Start of the simulation: 2019-02-02 23:35:27
End of the simulation: 2019-02-02 23:35:27
> 
> regData = function(n, SD){
+   data.frame(
+   x=seq(0,1,length=n),
+   y=rnorm(n, sd=SD))
+ }
> 
> eg <- eval_tibbles(
+   expand_tibble(fun="regData", n=5L, SD=1:2),
+   expand_tibble(proc="lm", formula=c("y~x", "y~I(x^2)")),
+   group_for_summary = "term",
+   replications=3
+ )
> eg
# A tibble: 12 x 7
   fun         n    SD replications proc  formula  results 
   <chr>   <int> <int>        <int> <chr> <chr>    <list>  
 1 regData     5     1            1 lm    y~x      <S3: lm>
 2 regData     5     1            1 lm    y~I(x^2) <S3: lm>
 3 regData     5     1            2 lm    y~x      <S3: lm>
 4 regData     5     1            2 lm    y~I(x^2) <S3: lm>
 5 regData     5     1            3 lm    y~x      <S3: lm>
 6 regData     5     1            3 lm    y~I(x^2) <S3: lm>
 7 regData     5     2            1 lm    y~x      <S3: lm>
 8 regData     5     2            1 lm    y~I(x^2) <S3: lm>
 9 regData     5     2            2 lm    y~x      <S3: lm>
10 regData     5     2            2 lm    y~I(x^2) <S3: lm>
11 regData     5     2            3 lm    y~x      <S3: lm>
12 regData     5     2            3 lm    y~I(x^2) <S3: lm>
Number of data generating functions: 2
Number of analyzing procedures: 2
Number of replications: 3
Estimated replications per hour: 346151
Start of the simulation: 2019-02-02 23:35:27
End of the simulation: 2019-02-02 23:35:27
> 
> presever_rownames = function(mat)
+ {
+   rn = rownames(mat)
+   ret = tibble::as_tibble(mat)
+   ret$term = rn
+   ret
+ }
> 
> eg <- eval_tibbles(
+   expand_tibble(fun="regData", n=5L, SD=1:2),
+   expand_tibble(proc="lm", formula=c("y~x", "y~I(x^2)")),
+   post_analyze = purrr::compose(presever_rownames, coef, summary),
+   #post_analyze = broom::tidy, # is a nice out of the box alternative
+   summary_fun = list(mean = mean, sd = sd),
+   group_for_summary = "term",
+   replications=3
+ )
Error in bind_rows_(x, .id) : 
  Argument 1 must be a data frame or a named atomic vector, not a summary.lm
Calls: eval_tibbles ... bind_or_combine -> do.call -> bind_rows -> bind_rows_
Execution halted