Deprecating impute_covariance_matrix

This is a maintenance release. The primary changes are:

• Deprecated impute_covariance_matrix() and pattern_covariance_matrix(), because they have been superseded by metafor::vcalc().
• Corrected a unit test related to the plm package, for compatibility with upcoming release of plm.

Fix for linear_contrast()

Fixed another bug in linear_contrast() to handle specified contrasts that are scalars when variance-covariance matrix is computed with a working model that is not inverse-variance.

Support for geeglm

This version adds support for geeglm() models from geepack. It also adds support for meta-analytic location-scale models estimated using the metafor package, along with miscellaneous bug fixes and enhancements.

• Added support for geepack::geeglm() models.
• Added support for rma.ls models (location-scale models estimated using metafor::rma.uni(scale = )).
• Improved error handling of Wald_test() when vcov of contrasts is not positive definite.
• Fixed a bug in linear_contrast() to handle specified contrasts that are scalars.
• Improved internal get_data function for gls and lme objects to allow use of expressions in addition to object names.

Suggestions

This version is a maintenance release that brings the package into compliance with CRAN policies on conditional use of packages listed in SUGGESTS.

• Fixed bug in methods for multi-variate multi-level models estimated with lme().
• Updated vignettes, examples, and unit tests so that the package can be compiled without any packages from SUGGESTS.

plm::plm() fixes and extensions

This release corrects a bug in the methods for random effects models fitted using plm() and extends the methods to work with nested random effects models estimated by plm().

• Corrected bug in methods for plm objects estimated by random effects, which occurred when a user-specified clustering variable was at a higher level than the random effects.
• Added support for plm objects with nested random effects (effects = "nested").
• Added additional syntactic options for specifying clustering variable with plm objects. See ?plm.
• Corrected bug in how Wald_test() labeled results when test = "Naive-Fp".

Confidence intervals with linear_contrast()

This release includes a new function linear_contrast(), which calculates robust confidence intervals and p-values for linear contrasts of regression coefficients from a fitted model. The function works with constrain_pairwise() and other constrain_*() helper functions.

Miscellaneous bug fixes.

This version includes miscellaneous bug fixes, error-handling, and corrections to the documentation.

• Wald_test() gains an option for test = "Naive-Fp", which uses denominator degrees of freedom equal to the number of clusters minus the number of coefficients in the fitted model.
• coef_test() and conf_int() gain an option for test = "naive-tp", which uses denominator degrees of freedom equal to the number of clusters minus the number of coefficients in the fitted model.
• Corrected a bug in the Satterthwaite degrees of freedom calculations for models that include only an intercept.
• Output of coef_test() and conf_int() now include a variable containing the coefficient names, so that the results are "tidy."
• conf_int() now includes an option to report a p-value for each coefficient.
• coef_test() now reports degrees of freedom for test = 'z' and test = 'naive-t'.
• vcovCR() now provides a more informative error message when the clustering variable is a constant.
• vcovCR() now handles models estimated using analytic weights, where some weights are equal to zero. Results are consistent with omitting observations with weights of zero.
• Added more informative error messages for Wald_test() and conf_int(), triggered if the test argument does not match any of the available tests.
• Corrected a bug in findCluster.rma.mv(), which threw an error if a random effects factor in the rma.mv model had unobserved levels.
• Corrected a bug in Wald_test(), which threw an error for tests of intercept-only models.
• Fixed a minor bug in print method for Wald_test() results, which threw an error when the p-value was NA.

New features for impute_covariance_matrix()

This release includes expanded functionality for impute_covariance_matrix() and a new, related function pattern_covariance_matrix(), both of which are useful for conducting meta-analysis of dependent effect sizes with robust variance estimation. It also includes a bug-fix for the method used to identify the outermost clustering variable in rma.mv objects.

• New functionality for impute_covariance_matrix():
  • Compute covariance matrices with AR1 correlation structure or with a combination of constant correlation and AR1 correlation structure.
  • Compute covariance matrices that are blocked by subgroup.
  • Average the variance estimates by cluster before computing covariance matrices.
• New function pattern_covariance_matrix(), which creates a covariance matrix based on a specified pattern of correlations between different categories of effects.
• Corrected bug in methods for rma.mv objects, which previously led to incorrect identification of clustering variables in models with multiple levels of random effects, where at least one set of random effects has inner | outer structure.

Redesigned Wald_test()

This release introduces a major update to Wald_test(), which now uses a set of helper functions (constrain_zero(), constrain_equal(), and constrain_pairwise()) to express constraints on the set of regression coefficients. For all the details, see the new vignette vignette("Wald-tests-in-clubSandwich").

The release also includes bug fixes for plm and robu methods.

• Major update to Wald_test()
  • Wald_test() now uses new helper functions constrain_zero(), constrain_equal(), and constrain_pairwise() to specify constraint matrices.
  • Wald_test() gains an argument tidy. When TRUE, results for a list of tests will be tidied into a single data.frame.
  • Output of Wald_test() now includes both numerator and denominator degrees of freedom.
• Corrected bug in methods for plm objects, which occurred when "within" models included cluster-level interactions. Previously main effects of cluster-level variables were not getting dropped from model_matrix.plm().
• Corrected bugs in methods for robu objects
  • Corrected a bug that previously led to errors for models with only one column in the model matrix (i.e., intercept-only models).
  • Corrected a bug in an internal function that previously led to errors in constrain_equal() and constrain_zero() when called on robu objects.

Another maintenance release

• Updated and streamlined unit tests for R 4.0.0.