whatifbandit 0.3.0
Breaking Change
plot.mab() for type = "assign" now
displays the proportion of total observations assigned to each treatment
for each period, instead of the individual probability of
assignment.
New Features
multiple_mab_simulation() calculates the number of
observations assigned to each treatment for each trial, and provides
support for plotting them has been added to
plot.multiple.mab() via the type = "hist" and
quantity = "assignment" arguments.summary.mab() now includes a new column with the number
of observations assigned to each treatment.summary.multiple.mab() now includes two new columns
with the mean and standard deviation for the number of observations
assigned to each treatment across the simulations.- Month-based assignment,
time_unit = "month" can now be
specified with and without an appropriate month_col,
resulting in either time-based (no month_col) or
calendar-based (provided month_col) assignments. See the
time_unit documentation for more details.
plot.multiple.mab() now accepts arguments for
ggplot2::facet_grid() for more precise customizations.
Other
whatifbandit 0.2.1
Bug Fixes
- Fixed handling of numeric and factor types in
condition_col of the data_cols argument.
- Weighting AIPW by group size along with adaptive weights.
- Fixed inconsistent results across with data.frames, tibbles, and
data.tables. Running
single_mab_simulation() or
multiple_mab_simulation(), with the same seeds on the same
system, now results in the same outcome regardless of input data
class.
whatifbandit 0.2.0
New Features
multiple_mab_simulation() supports parallel processing
via future.single_mab_simulation() and
multiple_mab_simulation() support data.table for
larger data sets.summary(), print(), and
plot() generics for mab and
multiple.mab class objects.single_mab_simulation() and
multiple_mab_simulation() throw informative error messages,
relating to argument specification, and data types passed.
whatifbandit 0.1.1
Bug Fixes
- Fixed AIPW calculations mistakes.
- Fixed improper random seeding in
multiple_mab_simulation().
- Improved numerical calculation errors in Thompson sampling with
large datasets.
- Optimization reduced simulation runtime by up to 50%.
whatifbandit 0.1.0
single_mab_simulation() and
multiple_mab_simulation() simulate successfully.