In the current tidytext document explaining about the tidy approach to stm object, there is no specific example of how to add covariates.

I wanted to try that out with stm::gadarian data using prevalence = ~treatment + s(pid_rep) covariate formula; however, I have fac

Goal: Make it clearer that the development version is, well, in development.

Original plan was to remove it from the landing pkgdown pages, but, since most of these are generated from the README.md, this would mean removing that information from the GH landing page as well.

tidyverse repos

• blob
• broom
• dbplyr
• dplyr tidyverse/dplyr#370

Right now it's not immediately obvious they're deprecated. They should either not have reference pages at all, or the reference page should just say, don't use X, use Y.

Increase code coverage by:

• Adding tests for quandl API and alphavantager API. Include encrypted API key to pass Travis.
• Review code cov and add tests as appropriate.

Branching this off of #259, the current error message returned by check_factor() references f:

<error/rlang_error>
`f` must be a factor (or character vector).
Backtrace:
  9. forcats::fct_reorder(., method.source, value2, median)
 10. forcats:::check_factor(.f)

The first argument of fct_reorder() is .f, as it is for check_factor(), which makes me think the error message

Currently we simulate from the model using a parametric bootstrap. We showed in the GLM vignette that this is valid if the model is "true", and the simulation is still relatively fast, but a parametric method would be more pleasing to me.

I'm not sure if there is any literature available on this.

A useR!2017 attendee asked after the implyr talk about what Impala's limitations for using DISTINCT were. Older versions of Impala (before version 2.0) allowed only one DISTINCT clause per query. Newer versions have removed this limitation. But the current version of Impala does have the limitation that you cannot use DISTINCT in more than one aggrega

Great and
very clear stepXstep package tutorial, Matt!.

A time-saving suggestion (if I may):
in Step:
"Examining the Results" (after Step 3),
where you have:

marketing_campaign_correlated_tbl %>%
filter(feature %in% c("DURATION", "POUTCOME", "PDAYS",
"PREVIOUS", "CONTACT", "HOUSING")) %>%
plot_correlation_funnel(interactive = FALSE, limits = c(-0.4, 0.4))

Why not "automatica