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Changelog

Source: NEWS.md

propertee 1.0.4

  • Fix routines for CR2/CV2 standard errors and associated degrees of freedom that previously errored when the provided teeMod had a non-empty na.action, or the teeMod or its weights argument had a dichotomy slot
  • Return NaN for the degrees of freedom when CR2/CV2 standard errors have deficient degrees of freedom
  • as.lmitt() finds StudySpecification objects passed to assigned()/a.()/z.()/adopters() when the object isn’t explicitly passed to as.lmitt()
  • Two routines for estimating degrees of freedom are now available: the default in summary.teeMod() is dof.type=stata, which uses the number of clusters (or observations, in the absence of clustering) less one. We suggest specifying dof.type="IK" in summary.teeMod() calls, which uses a routine adapted from Imbens and Kolesár (2016), which adapts a routine from Bell and McCaffrey (2002). This routine has been available in previous versions for summary.teeMod() calls with type=CR2, but it has been updated so it better matches the outputs of the dfadjust package maintained by Kolesár. See the documentation for the internal function .compute_IK_dof() for references and further details.

propertee 1.0.3

CRAN release: 2025-10-28

  • Fix bug in cov_adj() when covariance adjustment model formula includes transformations of variables

propertee 1.0.2

  • Fix bug in variance estimation code when the mean response/offset in a subpopulation of the control condition is not identified

propertee 1.0.1

CRAN release: 2025-08-26

  • Fix small test suite bugs

propertee 1.0.0

CRAN release: 2025-08-21

  • First CRAN submission

propertee 0.7.0

New Features

  • HC2 and CR2 standard errors are now available for teeMod objects. CR2 corrections use a new, fast computation that obviates the need for obtaining the spectral decomposition of orthocomplements of cluster-specific projection matrices.
  • Standard errors for teeMod objects now optionally include methodological developments from a forthcoming paper from Wasserman and Hansen:
    1. When computing the meat of the sandwich standard errors, the bias correction to the residuals from estimating intent-to-treat effects is allowed to differ from the bias correction to residuals from a covariance adjustment model fit. The type argument of vcov_tee() determines the correction for the former, while the cov_adj_rcorrect argument determines the latter. The findings from the simulation study in the paper inform the default arguments of vcov_tee() (and thus, summary.teeMod()): an HC2/CR2 correction for the residuals of the intent-to-treat effect estimates, and an HC1/CR1 correction or the residuals from the covariance adjustment model fit.
    2. When units used for estimating intent-to-treat effects also contribute to fitting the covariance adjustment model, the boolean loco_residuals argument of vcov_tee() indicates the residuals from effect estimation associated with individuals in the overlapping units should be replaced with residuals that use a leave-one-out estimate of the covariance adjustment model parameters.
  • Degrees of freedom provided in summary.teeMod() now reflect clustering: for CR0 and CR1 standard errors, the associated degrees of freedom are one less than the number of clusters used for estimation; for CR2 standard errors, degrees of freedom are computed using the approach of Imbens and Kolesár (2016) (see documentation of .compute_IK_dof() for citation) and leverage the fast computational routine mentioned above
  • Units of assignment in StudySpecification are now optional, though recommended.

Bug Fixes

  • Fix minor bugs with the dichotomy argument of lmitt()

propertee 0.6.1

Bug Fixes

  • unit_of_assignment(), unitid(), cluster(), uoa(), block(), and forcing() no longer fail automatically when passed a non-numeric or non-character variable. Now, they will first attempt to convert the variable to a character variable.
  • An internal routine in vcov_tee() no longer fails when the environment in which vcov_tee() is called differs from the environment in which the StudySpecification associated with the teeMod is created.

propertee 0.6.0

New Features

  • In their coefficients element, teeMod objects now report estimates of mean quantities in the control condition (response and, if applicable, predictions of response). See the lmitt() man page for further details.
  • Introduces etc() (effect of the treatment on controls) and ato() (overlap-weighted effect) weighting functions. atc is an alias for etc(), while olw, owt, and pwt are aliases for ato().
  • Bump minimum R version to 4.1.0 to allow internal usage of pipes and anonymous functions.

Bug Fixes

  • When ate()-type functions are called with data arguments that do not include rows associated with all units of assignment specified in the StudySpecification object, the resulting weights reflect assignment probabilities across all units of assignment in the StudySpecification, not only those represented in data.

propertee 0.5.2

New Features

  • Users fitting multiple teeMod objects to test multiple outcome variables or different levels of a factor treatment variable can pass those models to an mmm object from the multcomp package, then pass the mmm object to multcomp’s glht() function to obtain standard errors estimated using vcov_tee(). This requires passing vcov=vcov_tee to glht(). On a technical note, **propertee** now “suggests” installing multcomp.

Bug Fixes

  • Weights produced by ate() and ett() no longer produce 0’s or NA’s for StudySpecification objects created with multiple columns specifying the blocking scheme

propertee 0.5.1

Bug Fixes

  • Variance estimation routine fixes miscalculations in the case when there is covariance adjustment and rows are omitted from the lmitt() fit due to NA’s in the covariates or treatment assignment

propertee 0.5.0

Major Changes

  • All references to “design” have been changed to “specification”.
    • *_design is now *_spec (e.g. rct_design is now rct_spec)
    • Design objects are now StudySpecification objects
    • The design= argument to lmitt() is now specification=.

propertee 0.4.1

New Features

  • Passing absorb = TRUE to lmitt without specifying a block proceeds as if the entire sample is a single block.

Bug Fixes

  • Fixed bug where use of dichotomy and moderator variables in lmitt() could lead to errors due to too long of a formula.

propertee 0.4.0

New Features

  • lmitt(), weights calculation functions ate() and ett(), and assignment vector generation function assigned() now accept a dichotomy argument that can be used for studies with time-varying treatment assignment. The Design object, unlike before, will not carry information about this dichotomization. Instead, the information stored there reflecting when units were assigned to treatment (if they were assigned to treatment) will be leveraged to create inverse probability of assignment weights and assignment indicators for datasets that have longitudinal data for the study units.

Bug Fixes

  • Standard error calculations no longer error when a by column is used to uniquely identify rows in the covariance adjustment or effect estimation sample that cannot be distinguished with information in the Design alone

propertee 0.3.10

Bug Fixes

  • Linking unit of assignments to clusters for variance estimation no longer errors when Design objects are created with a tibble
  • cov_adj() does not error with covariance adjustment models fit with robustbase::glmrob()

propertee 0.3.9

Bug Fixes

  • Scaling constants have been updated in estfun.teeMod() to account for a previously missing factor of sqrt(n / n_C) applied to contributions to the covariance adjustment model estimating equations

propertee 0.3.8

Breaking Changes

  • When model-based standard errors clustered at the level of assignment are called for in a blocked design, vcov_tee() clusters units of assignment in small blocks, blocks with only one treated or control unit, together.

propertee 0.3.7

Breaking Changes

  • vcov_tee() scales estimating equations using different constants than it did before

propertee 0.3.6

Bug Fixes

  • Previous procedure for aligning contributions to estimating equations from first-stage and second-stage models failed when column(s) used for alignment had NA’s. Outputs of vcov_tee() were liable to change from call to call as a result. This has been fixed.

propertee 0.3.5

Improvements

  • Diagonal elements of vcov_tee() matrices lacking sufficient degrees of freedom for estimation are returned as NA’s rather than numeric zeros. This is a deviation from the sandwich package that aims to provide clarity to results that may otherwise appear as negative diagonal elements of the vcov matrix

Bug Fixes

  • When lmitt() is called with a blocked design and absorb=TRUE, the block-centered assignment and, if applicable, moderator and assignment:moderator interaction columns, are no longer centered on the grand mean of the column. This ensures blocks that do not satisfy positivity of the assignment variable (or positivity within a factor level) do not contribute to effect estimation
  • lmitt() now accepts references to formula objects

propertee 0.3.4

Improvements

  • Computational performance for estfun.teeMod has been improved

Bug Fixes

  • No more errors due to under-the-hood duplication of a moderator variable
  • absorb=TRUE estimates have been corrected in the case when all observations in a stratum have 0 weights due to only treated or control units of assignment existing in the stratum

propertee 0.3.3

Added Features

  • vcov_tee() can accept user-created variance estimation functions that start with the prefix .vcov_; the type argument should take the rest of the function name as an input
  • Variance estimation for robust GLM’s (models fit using robustbase::glmrob) is now accommodated
  • HC1 variance estimates are now accommodated

propertee 0.3.2

Added Features

  • Effect estimation for continuous moderator variables is now supported

Non-Breaking Changes

  • vcov_tee() will return NA’s for the entries of the covariance matrix that lack sufficient degrees of freedom for an estimate. Informative warnings will accompany the matrix, further indicating which standard errors have been NA’d out.

Bug Fixes

  • Functions for generating weights, ate() and ett(), return weights of 0 rather than infinity for blocks that contain treated units but no control units.
  • Prior covariate adjustment fits were previously incorporated into variance estimation differently depending on whether one created a SandwichLayer object before calling lmitt() or called cov_adj() in the offset argument of the lmitt() call. This has been corrected, and both ways return the same variance estimates.
  • Covariate adjustment models that admit rectangular bread matrices, such as those produced by robustbase::lmrob, are now accommodated given the reformulated estimating equations in versions v0.1.1 and later.
  • A contrasts error raised by model.matrix() in certain cov_adj() calls has been resolved.

propertee 0.3.1

Breaking Changes

  • We now order teeMod objects’ matrix of estimating equations based on user-specified ID columns or unit of assignment ID’s.
  • The stats::update function can no longer be called on teeMod objects.

Non-Breaking Changes

  • teeMod objects now have lmitt_call slots.
  • summary calls on teeMod objects accept vcov.type arguments to specify the desired standard error calculation shown in the output. Acceptable types follow the documentation for vcov_tee.
  • Shown or printed teeMod objects return more comprehensible labels for ITT effect outputs.

R Version Compatibility

  • Now compatible with R 4.3. Particularly, we advise users working with R 4.3 to avoid expand.model.frame calls on teeMod objects and instead use the internal function .expand.model.frame_teeMod when necessary.

propertee 0.2.1

Breaking Changes

  • Stratum fixed effects and subgroup moderating effects can now be accounted for via the absorb argument. Previous versions did not properly support this functionality. Valid standard errors under absorption, however, have not been confirmed.

propertee 0.1.1

Breaking Changes

  • We have reformulated the estimating equations used to derive standard errors. In estimation settings we accommodate, testing has not revealed any differences in standard error estimates between the previous and current estimating equations, but we do not assure this is the case for all possible situations.

propertee 0.0.1

  • Compatible with R 4.2.3
  • Introduces functionality for direct adjusted and design-informed standard errors accommodating covariance adjustment in the model-based setting
  • Cluster-robust standard errors can only be estimated using the HC0 estimator