feature request: supporting metaplus models

[IndrajeetPatil]

Of course, this needs to be de-tidyversified, but broadly this is the kind of output I would expect.

# setup
set.seed(123) 
library(metaplus)
#> 
#> Note: The second parameter is the study standard error not the standard error squared as in the metafor package.
library(tidyverse)

# perform meta-analysis for mag studies, which have no outliers
# fit standard normal random-effect model 
mag.meta <- metaplus(yi, sei, slab = study, data = mag)

# class
class(mag.meta)
#> [1] "metaplus"

# summary
summary(mag.meta)
#>          Est. 95% ci.lb 95% ci.ub   pvalue
#> muhat -0.7463   -1.2583   -0.3428 0.000501
#> tau2   0.2540                             
#> 
#>      logLik      AIC      BIC
#>   -19.68459 43.36918 44.91436

# possible output 
mag.meta %>% {
  dplyr::inner_join(
    x = as_tibble(as.data.frame(.$results), rownames = "term"),
    y = as_tibble(as.data.frame(.$profile@summary@coef), rownames = "term"),
    by = "term"
  ) %>%
    dplyr::rename_all(.tbl = ., .funs = tolower) %>%
    dplyr::select(
      .data = .,
      term,
      estimate,
      conf.low = `95% ci.lb`,
      conf.high = `95% ci.ub`,
      p.value = pvalue,
      z.value = `z value`
    ) %>%
    dplyr::filter(.data = ., term == "muhat")
}
#> # A tibble: 1 x 6
#>   term  estimate conf.low conf.high  p.value z.value
#>   <chr>    <dbl>    <dbl>     <dbl>    <dbl>   <dbl>
#> 1 muhat   -0.746    -1.26    -0.343 0.000501      NA

^{Created on 2020-06-10 by the reprex package (v0.3.0)}