# **propertee** 1.0.5
## Updates
* `vcov_tee()` accepts a new `values_from` argument: users may pass a fitted model object whose residuals and weights will be used for estimating standard errors. This fitted model could be fit under specified parameter restrictions, for example.
* Model-based standard errors no longer by default cluster at the block level for fine strata (blocks where the treatment or control condition are assigned a lone unit). See further discussion in Wasserman's 2026 thesis "Methods for causal inference in settings with clustered data subject to missingness and measurement error".

## Bug Fixes
* Fix bug in `ate()/ett()/...` when units of assignment are identified in a `StudySpecification` by the rownames of a dataframe, that is, the `StudySpecification` is created by a call of the form `*_spec(trt ~ 1 + ..., data)`
* Fix bug that allowed observations with zero weight to figure in degrees of freedom calculations for moderator estimates

# **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
* 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
* Fix small test suite bugs

# **propertee** 1.0.0
* 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
