The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
launchApp() now starts a new, modernised Shiny app
with a guided five-step workflow (Data, Setup, Pre-tests, Sampling,
Report), example data, an HTML report and Excel/CSV exports. The app’s
extra packages are listed under Suggests; launchApp()
reports any missing ones, and passes ... on to
shiny::runApp().
VCG_sampler(): the size sanity warnings now count
the POOL as the first level of the group variable, so they are also
correct for combine_data() output (“POOL”/“TG”) instead of
reporting a POOL size of 0.
VCG_sampler() now stops with a clear message when
n is larger than the POOL with complete data, or when one
of the groups is empty, instead of silently selecting the whole
POOL.
VCG_sampler() with stratum now returns the rows in
the input order and keeps rows with a missing stratum value (with
VCG = NA), as is already done for rows with missing
covariates.
BestVCGsize() no longer fails for small POOLs or
when no plateau is found (it returns a single NA), only
scans sizes up to the POOL size with complete data, and no longer
repeats the VCG_sampler() size warnings for every tried
size.
robust_scale() ignores NA when
detecting binary/categorical variables and no longer returns
Inf/NaN when the reference group is
constant.
plot_var() now works with a single stratum level and
with character stratum variables.
VCG_sampler() now shortens long covariate names in
the target plot to a maximum of 11 characters, built from the first 8
characters + _ + the last 2 characters to keep them unique.
A warning is printed when names are cut.
VCG_sampler() now issues sanity warnings when
running: too many covariates (> 21), a small POOL size relative to
the number of covariates (ratio < 5, reported), and a requested VCG
size that is >= 75% of the POOL. When stratification is used, these
checks are reported per stratum, plus a warning is added when stratum
sizes are strongly imbalanced.
Covariate scaling now uses a robust median/MAD approach (falling
back to the standard deviation when the MAD is 0).
robust_scale() gained an optional group
argument (defaulting to NULL, using all observations) and
now backs the scaling in VCG_sampler(),
energy_distance(), combine_variables() and
find_outliers(), replacing base
scale().
Unit test added via testthat framework
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.