Support for using glm_weightit() in the outcome model
after weighting using weightthem() is added.
New functions as.mimids() and as.wimids()
are now available; these take a list of matchit or
weightit objects fit to multiply imputed data and transform
them into a mimids or wimids object,
respectively, for use in balance assessment and to take advantage of
MatchThem’s other capabilities.
In this update, the matchthem() function has been
enhanced to include support for the quick and
cardinality matching methods. These additional methods
offer users more options for their matching analyses, catering to a
wider range of use cases and data characteristics. Alongside the
matching method updates, this release also incorporates improved
documentation and inclusion of a newly introduced weighting approach by
Nguyen and Stuart, known as averaging probability weights
(apw, please see their paper here).
This update is dedicated to enhancing the documentation, providing clearer explanations, examples, and instructions to improve the usability and understanding of the package. The focus is on ensuring that users have comprehensive and detailed documentation to make the most out of the package’s features and functionality.
This update is primarily focused on enhancing the documentation and addressing minor bugs in order to improve the overall performance and user experience of the package. The necessary fixes have been implemented to ensure smoother operation and to address any reported issues.
This update brings significant improvements to the documentation and
introduces several new features: 1. The mimira and
mimipo objects, which are the output of with()
and pool() functions respectively, now inherit from the mice
classes mira and mipo. This allows for
seamless integration with existing mice
methods, 2. When using coxph() with with(),
the update ensures that the robust standard errors are correctly
applied, 3. A new cluster argument has been added to
with.mimids() function. This argument controls whether
cluster-robust standard errors should be used to account for pair
membership when the model is a svyglm()-type model from the
survey
package. By default, pair membership is included when present and there
are 20 or more unique subclasses (pairs), 4. The cbind()
methods have been documented and exported, 5. The mimids
and wimids objects have been optimized to reduce their
size. They now only contain the supplied mids object and
the outputs from matchit() or weightit(), and
6. A new trim() function has been added to trim estimated
weights, utilizing WeightIt::trim() with the same syntax
(credits go to Nicolas for this contribution).
These updates enhance the functionality, flexibility, and efficiency of the package, providing users with an improved experience.
This update focuses on resolving a few bugs, resulting in improved stability and functionality of the package. The necessary fixes have been implemented to address these issues and enhance the overall performance.
This update includes a modification to the complete()
function in order to avoid any potential conflicts with the tidyr
package. The updated definition of the complete() function
ensures smooth compatibility and eliminates any name clashes that might
have occurred.
This update focuses on enhancing documentation and addressing minor bugs, resulting in improved overall performance and user experience.
This update focuses on enhancing documentation and implementing
compatibility for robust estimation of standard errors. Specifically,
the package now supports compatibility with the svyglm()
and svycoxph() functions from the survey
package, allowing them to be used as expressions within the
with() function. Additionally, new matching and weighting
methods have been introduced, such as the full,
genetic, and cem matching methods, as well as
the ebal and optweight weighting methods.
Furthermore, the package now includes the complete()
function, which replaces the previous matchthem.data() and
weightthem.data() functions. This new function provides
improved functionality and convenience for completing the required data
operations within the package.
This update focuses on enhancing the documentation and addressing minor bugs to improve the overall quality of the package. It includes necessary refinements and fixes to ensure a more polished and seamless user experience.
The MatchThem
package has been released and can now be accessed on Github and Comprehensive R
Archive Network (CRAN).
