Update returned harmonized data object + NEWS to v0.0.3.4

[lcpilling]

Changes:

1. MRlap now returns the "harmonised_mr_data" i.e., the IVs used in the IVW. This PR updates the naming to be consistent with format for TwoSampleMR::mr() -- see below example -- we were finding it a bit tedious to keep formatting the data in multiple ways to perform additional downstream sensitivity analysis
2. The "NEWS" was a few versions out of date

Example of how update affects using MRlap in a pipeline:

# run standard MRlap analysis
mrlap_res = MRlap::MRlap(
  exposure = gwas1,
  exposure_name = "gwas1",
  outcome = gwas2,
  outcome_name = "gwas2",
  ld = ld,
  hm3 = hm3)

# get harmonised IVs
dat <- mrlap_res$harmonised_mr_data

# run other TwoSampleMR analyses
res <- TwoSampleMR::mr(dat)

# get scatter plot of IVs with "standard" estimates plotted
p1 <- TwoSampleMR::mr_scatter_plot(res, dat)
p[[1]]

# add MRlap corrected estimate line
p[[1]] + 
  ggplot2::geom_abline(intercept=0, slope=mrlap_res$MRcorrection$corrected_effect, color="red")

[lcpilling]

Another small quality of life improvement added to this branch... MRlap can now take GWAS catalog formatted summary statistics using their default headers

[lcpilling]

Actually just realised it is really easy to bundle a data frame of the TwoSampleMR output with additional row for corrected IVW effect.

Means that the output of MRlap can be inspected a plotted very easily now:

# run standard MRlap analysis
mrlap_res = MRlap::MRlap(
  exposure = gwas1,
  exposure_name = "gwas1",
  outcome = gwas2,
  outcome_name = "gwas2",
  ld = ld,
  hm3 = hm3)

# check out results table
mrlap_res$results_table
#>   id.exposure id.outcome outcome exposure                                method nsnp         b         se        pval
#> 1        GORD        CRS     CRS     GORD             Inverse variance weighted   38 0.1258992 0.05436226 0.020562052
#> 2        GORD        CRS     CRS     GORD Inverse variance weighted (corrected)   38 0.1545762 0.07318573 0.034677164
#> 3        GORD        CRS     CRS     GORD                       Weighted median   38 0.1578384 0.05514316 0.004205286
#> 4        GORD        CRS     CRS     GORD                           Simple mode   38 0.2228619 0.11716894 0.064970474
#> 5        GORD        CRS     CRS     GORD                         Weighted mode   38 0.2078488 0.11262181 0.072972196
#> 6        GORD        CRS     CRS     GORD                              MR Egger   38 0.7187816 0.55849830 0.206311804

# get scatter plot of IVs with "standard" estimates & the new corrected estimate too
p1 <- TwoSampleMR::mr_scatter_plot(mrlap_res$results_table, mrlap_res$harmonised_mr_data)

I know trivial updates but just saves incremental time each time I run things