Convert mb_weight() to data.table

[jdblischak]

I chose mb_weight() as the next function to convert since it would allow us to move tidyr from Imports to Suggests.

The speed increase was ~2x, but that wasn't the primary motivation in this case.

library("microbenchmark")

# before
microbenchmark(mb_weight(x, delay = 6, w_max = Inf), times = 1000)
## Unit: milliseconds
##                                  expr     min      lq    mean   median       uq      max neval
##  mb_weight(x, delay = 6, w_max = Inf) 11.0001 11.7249 13.5589 12.19605 13.46255 186.3355  1000

# after
microbenchmark(mb_weight(x, delay = 6, w_max = Inf), times = 1000)
## Unit: milliseconds
##                                  expr    min      lq     mean median      uq      max neval
##  mb_weight(x, delay = 6, w_max = Inf) 3.8054 4.19625 5.571988 4.5562 5.48045 168.4506  1000

Also note that I had to temporarily disable skip_if_offline() because it uses r-project.org, which has been down all morning

xref: #111

[jdblischak]

I also made a few updates to the vignette that explains mb_weight(). I think the section Freidlin and Korn strong null hypothesis example needs some more attention. For example, I don't think this line below about the failure rate is correct:

simtrial/vignettes/modestWLRTVignette.Rmd

Line 129 in 0f06d96

The control group has an exponential failure rate with a median of 15 months.

[jdblischak]

Also, it would be ideal to add a test case that involves a stratum that gets dropped by the filter:

simtrial/R/mb_weight.R

Lines 153 to 156 in 0f06d96

	# Get back stratum with no records before delay ends
	right_join(tbl_all_stratum, by = "stratum") %>%
	# `max_weight` is 1 when there are no records before delay ends
	mutate(max_weight = replace_na(max_weight, 1)) %>%

[nanxstats]

This looks good to me - you will want to check in with the original author of vignettes/modestWLRTVignette.Rmd about the minor changes.