## Package Updates

### Changes in Version 1.0.3 (2023-05-18)

-   Fixed an error in the `gformula()` function that assumed that the
    name of the ID variable in `obs_data` was `'id'`
-   Removed Travis CI

### Changes in Version 1.0.2 (2023-02-27)

-   Revised the plot of the estimates of the natural course risk so that
    it starts at (0, 0)
-   Fixed an error when obtaining confidence intervals around the hazard
    ratio estimates
-   Fixed an error in the reported standard errors of the coefficients
    of the fitted categorical covariate models
-   Fixed an error in the reported root mean squared error values for
    the outcome and competing event models
-   Allowed categorical covariates to be of class “numeric” (rather than
    requiring them to be of class “factor”)

### Changes in Version 1.0.1 (2023-01-11)

-   Added the “cumulative percent intervened on” and “average percent
    intervened on” to the output of the `gformula()` function
-   Added option for users to carry forward the natural value of
    treatment rather than the intervened value. See the `int_visit_type`
    argument in the `gformula()` function
-   Added option for users to access the bootstrap replicates of the
    parametric g-formula estimates. See the `boot_diag` argument in the
    `gformula()` function.
-   Fixed an error in computing the inverse probability weighted means
    of the time-varying covariates

### Changes in Version 1.0.0 (2022-04-09)

-   Added option for users to specify censoring models to compute
    inverse probability weights for estimating the natural course means
    / risk from the observed data
-   Added data set `censor_data` and a corresponding example application
    in the documentation to illustrate the application of inverse
    probability weighting for estimating the natural course means / risk
    from the observed data
-   Fixed an error in calculating the means of the time-varying
    covariates under the natural course for survival outcomes
-   Fixed errors in calculating the observed risk estimates and
    g-formula survival estimates when competing events are not treated
    like censoring events
-   For categorical time-varying covariates, the
    `plot.gformula_survival()`, `gformula_continuous_eof()`, and
    `gformula_binary_eof()` functions now display the nonparametric/IP
    weighted and parametric g-formula estimates of the probability of
    observing each level of the covariate. Previously, these functions
    displayed the counts of categorical variables.

### Changes in Version 0.3.2 (2021-07-13)

-   Updated computation of (lagged) cumulative averages to use the
    recursive formula. There should be a noticeable improvement in the
    computation time when using several (lagged) cumulative average
    terms and when the number of time points is large.
-   Fixed an error for covariates of type `truncated normal` (Thanks to
    @publichealthstudent)
-   Updates to the documentation

### Changes in Version 0.3.1 (2020-03-22)

-   Fixed error in the `coef.gformula()` example

### Changes in Version 0.3.0 (2020-01-30)

-   Added wrapper function called `gformula()` for the
    `gformula_survival()`, `gformula_continuous_eof()`, and
    `gformula_binary_eof()` functions. Users should now use the more
    general `gformula()` function to apply the g-formula.
-   Added option for users to specify the values for lags at
    pre-baseline times by including rows at time -1, -2, …, -i.
-   Added an example data set called `continuous_eofdata_pb`, which
    illustrates how to prepare a data set with pre-baseline times
-   Added option for users to pass in “control parameters” (e.g.,
    maximum number of iterations, maxit, in glm.control) when fitting
    models for time-varying covariates via the `covparams$control`
    argument. (Thanks to @jerzEG for the suggestion)
-   Added option for users to access the fitted models for the
    time-varying covariates, outcome, and competing event (if
    applicable). See `model_fits` argument of the `gformula()` function
-   Added simulated data under the natural course to the `sim_data`
    component of the output of the `gformula()` function
-   Added a progress bar for the number of bootstrap samples completed.
    See the `show_progress` argument of the `gformula()` function for
    further details
-   Added `summary()`, `coef()`, and `vcov()` S3 methods for objects of
    class ‘gformula’
-   Added argument `fits` in the `print.gformula_survival()`,
    `print.gformula_continuous_eof()`, and `print.gformula_binary_eof()`
    functions. Added argument `all_times` in the
    `print.gformula_survival()` function
-   Fixed minor bug in the `lagavg()` function
-   Fixed bug occuring when not using lags of the intervention
    variable(s)
-   Fixed bug occuring in the truncation beyond covariate ranges.
    (Thanks to Louisa Smith)
-   Updates to the documentation

### Changes in Version 0.2.1 (2019-08-24)

-   First version released on CRAN
    (<https://CRAN.R-project.org/package=gfoRmula>)
-   Updates to the documentation

### Changes in Version 0.2.0 (2019-08-22)

-   Removed `example_intervention1()`, `example_intervention2()`, and
    `visit_sum_orig()`, as these functions are not used internally and
    users should not directly apply them
-   Removed export of `visit_sum()` and `natural()`, as these functions
    are used internally and users should not directly apply them
-   Updates to the documentation

### Changes in Version 0.1.1 (2019-08-21)

-   Minor updates to the documentation

### Changes in Version 0.1.0 (2019-08-17)

-   First version released on GitHub
    (<https://github.com/CausalInference/gfoRmula>)
