The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
Package Updates
Changes in Version 1.1.0
(2024-09-30)
- Added a new approach for specifying interventions in the
gformula() function. See the vignette “A Simplified
Approach for Specifying Interventions in gfoRmula”.
- Added option for users to specify custom outcome models in the
gformula() function. See the vignette “Using Custom Outcome
Models in gfoRmula”.
- Added the option to not truncate covariates simulated from a normal
distribution. See the argument
sim_trunc to the
gformula() function
- Fixed a bug occuring when using covariates of type
"categorical time"
- Fixed an issue where the point estimates differed when changing the
number of bootstrap samples. Since this fix involved adding a
set.seed statement, point estimates can be numerically
different from previous versions of the package.
- Added unit tests.
Changes in Version 1.0.4
(2024-01-30)
- Fixed an error for joint interventions on multiple treatments
- Fixed an error occurring when multiple restrictions are applied to a
single variable
- Revised the
gformula() function so that it produces a
warning message rather than an error message when one of the bootstrap
replicates fails. The bootstrap standard errors and 95% CIs are
calculated based on the bootstrap replicates that do not fail.
- Fixed an error occurring when no interventions are supplied (i.e.,
only the natural course intervention is used)
- Slightly sped up the calculation of the counterfactual cumulative
risks
- Expanded the error checking
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
- 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)
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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.