rsimsum 0.6.0


Bug fixes:

rsimsum 0.5.2

Bug fixes:

rsimsum 0.5.1

Bug fixes:

rsimsum 0.5.0


Bug fixes:

rsimsum 0.4.2

Implemented autoplot method for simsum and summary.simsum objects; when calling autoplot on summary.simsum objects, confidence intervals based on Monte Carlo standard errors will be included as well (if sensible).

Supported plot types are:

Several options to customise the behaviour of autoplot, see ?autoplot.simsum and ?autoplot.summary.simsum for further details.

rsimsum 0.4.1

Fixed a bug in dropbig and related internal function that was returning standardised values instead of actual observed values.

rsimsum 0.4.0

rsimsum 0.4.0 is a large refactoring of rsimsum. There are several improvements and breaking changes, outlined below.


Breaking changes

rsimsum 0.3.5

Breaking changes

rsimsum 0.3.4

rsimsum 0.3.3

rsimsum 0.3.3 focuses on improving the documentation of the package.

Improvements: * Improved printing of confidence intervals for summary statistics based on Monte Carlo standard errors; * Added a description argument to each get_data method, to append a column with a description of each summary statistics exported; defaults to FALSE; * Improved documentation and introductory vignette to clarify several points (#3, @lebebr01); * Improved plotting vignette to document how to customise plots (#4, @lebebr01).

New: * Added CITATION file with references to paper in JOSS.

rsimsum 0.3.2

rsimsum 0.3.2 is a small maintenance release: * Merged pull request #1 from @mllg adapting to new version of the checkmate package; * Fixed a bug where automatic labels in bar() and forest() were not selected properly.

rsimsum 0.3.1

Bug fixes: * bar(), forest(), lolly(), heat() now appropriately pick a discrete X (or Y) axis scale for methods (if defined) when the method variable is numeric; * simsum() and multisimsum() coerce methodvar variable to string format (if specified and not already string); * fixed typos for empirical standard errors in documentation here and there.

Updated code of conduct ( and contributing guidelines (

Removed dependency on the tidyverse package (thanks Mara Averick).

rsimsum 0.3.0

Bug fixes: * pattern() now appropriately pick a discrete colour scale for methods (if defined) when the method variable is numeric.

New plots are supported: * forest(), for forest plots; * bar(), for bar plots.

Changes to existing functionality: * the par argument of lolly.multisimsum is now not required; if not provided, plots will be faceted by estimand (as well as any other by factor); * updated Visualising results from rsimsum vignette.

Added and

rsimsum 0.2.0

Internal housekeeping.

Added S3 methods for simsum and multisimsum objects to visualise results: * lolly(), for lolly plots; * zip(), for zip plots; * heat(), for heat plots; * pattern(), for scatter plots of estimates vs SEs.

Added a new vignette Visualising results from rsimsum to introduce the above-mentioned plots.

Added x argument to simsum and multisimsum to include original dataset as a slot of the returned object.

Added a miss function for obtaining basic information on missingness in simulation results. miss has methods print and get_data.

rsimsum 0.1.0

First submission to CRAN. rsimsum can handle:

Summary statistics that can be computed are: bias, empirical standard error, mean squared error, percentage gain in precision relative to a reference method, model-based standard error, coverage, bias-corrected coverage, and power.

Monte Carlo standard errors for each summary statistic can be computed as well.