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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

• First submission to CRAN

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.

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.