Comments (3)
Ahh, perhaps this was the original reason?
from matchit.
Simply speaking, it's because MatchIt
only needs optmatch
if you are using optimal or full matching, which are not necessarily the right methods to use for a given application. Any packages that are not required for all methods (or for the default method) are in Suggests
and not Imports
because we should not require users to install every package when they are only using one matching method. This also keeps the package robust in case a package is removed from CRAN, which has happened in the past. I believe the package would be fully compliant if any of the Suggest
ed packages were removed from CRAN; one could still build the package and use the matching methods that do not depend on that package.
The help page for each matching methods lists the packages that are required for that method to work.
from matchit.
That's helpful - thanks.
from matchit.
Related Issues (20)
- Sensitivity Analysis with a MatchIt Object - Suggestion HOT 1
- Cardinality matching doesnt work with HiGHS HOT 1
- Relation between Covariates for Matching and Variables for Treatment Effect Estimation HOT 1
- Data needs to be randomized before sending it to matchit. Is this desired behaviour? HOT 7
- Weights for t test, chi square test, and effective sample size of subgroups HOT 5
- Match selected samples from both control and treatment cohort HOT 4
- Can glm.nb be used after running matching with Matchit? HOT 1
- Large Dataset with MatchIT HOT 13
- Issue with using exact statement within optimal matching (using MatchIt) HOT 14
- Matching strategy for clusters that don't include both treatment groups - multilevel matching HOT 3
- Issue regarding the unit.id argument HOT 4
- ATT estimate in survival analysis HOT 2
- best way to estimate interactions? HOT 7
- Error installing "MatchIt" HOT 1
- Cardinality matching fails when there is only one covariate
- Running tests fails: Error: could not find function "avg_comparisons", "boot.ci" HOT 3
- Obtaining odds ratios vs risk ratios using avg_comparisons() HOT 1
- Predetermined subsample size HOT 1
- K-nearest neighbor matching for ATE and missing match.matrix HOT 2
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from matchit.