NEWS.md
In gfoRmula: Parametric G-Formula

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

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”)

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

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.

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

Fixed error in the coef.gformula() example

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

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

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