- fixed some interals to work with
`survival`

package version 3.0 - added a
`conf_level`

argument that can set the width of the confidence intervals for the predicted cumulative hazards

- release version
- updated CITATION info and documentation

- fixed a bug that would occur in predict() when strata() was also used.

- minor fixes, and by default predict() now doesn’t calculate CI for the hazard function (it’s slow and most people don’t need that anyway)

- fixed an error that would happen in recognizing the
`autoplot()`

method on Linux systems

- fixed the vignette and made the plots nicer

- Nicer print in summary() and print()
- Added a sort of safety net when the likelihood is very flat. The program will switch then from
`nlm()`

to`optimize()`

, which is generally more stable in these cases. The reason why I do not switch all the time top`optimize()`

is because that one cannot be programmed with. Then, in combination with`numDeriv::hessian`

, estimation would take forever and be basically impossible to check.

- Fixed a bug where se = FALSE would break the predict() method

- Added the
`zph`

option in`emfrail_control()`

so that the result of the`cox.zph`

for the frailty model is also returned. This can be used to for goodness of fit. A guide on that soon to come! - Bugfix (when empty strata was part of the input)

Major update. Now stratified models are supported! Several improvements in the documentation and in the performance section.

Smaller fixes, as compared to the previoius CRAN release:

- removed
`rev(cumsum(rev(rowsum)))`

statement and replaced with an Rcpp function`rowsum_vec`

- using the cholesky decomposition instead of
`solve`

, seems that this is way better for symmetric matrices (0.7.16) - simplified a bit the
`emfrail_control()`

function (0.7.15) - documented options in
`summary()`

that control what is printed (if you want the output of a package to fit on one slide, for example) (0.7.15) - fixed a bug in
`ca_test`

that was not reading correctly the input because of the partial matching of arguments in R (0.7.14) - fixed some inconsistencies in summary and predict methods, when certain options are passed (such as no standard errors, for example) (0.7.13)
- improved print method for the summary object with some options to make things shorter (0.7.12)
- improved behaviour when frailty variance is actually 0 (0.7.11)
- fixed some warnings that were actually expected behaviour (0.7.10)
- some improvements in the limiting case where there is no frailty (0.7.10)
- more consistent notation and less vague passing of limits for the confidence intervals (now it’s clear whether it’s log scale or not) (0.7.10)

As compared to the previous CRAN release, 0.7.2: - fixed a bug where the estimation would go wrong when the data set was not ordered according to the cluster - fixed a bug where `emfrail`

would crash when the cluster colum would be a character vector - fixed a bug where the test for dependent censoring would not work - part of the output is now nicer (e.g. the `frail`

vector is named, the `autoplot.emfrail()`

gives a nicer plot) - removed a bunch of redundant calculations and old pieces of code - minor corrections in the vignette

As compared to the previous CRAN release, 0.7.0:

`ca_test()`

now provides an interface to use the Commenges-Andersen test for heterogeneity outside the`emfrail()`

function. It takes as input a`coxph`

object. Therefore, it can work with other baseline hazard estimators and with strata.- Various fixes in the documentation and vignette, mostly typos.

As usual, feedback is welcome.

- Various fixes for
`ca_test()`

: no more model frame needed, works well with strata. - fixed some small things in vignette

- fixed some comments and some documentation
- fixed the
`ca_test()`

, a small bug that was leading to wrong answers sometimes. Now it should give the sam result as the one in`emfrail`

.

`ca_test()`

now works for`coxph`

models properly as long as they have covariates- fixed a bug where the CA test would not give the correct results in
`emfrail`

.

- added a ca_test() function for
`coxph`

objects. Basically this is also done in`emfrail()`

, but now you can also use`strata`

or other things that are not supported by`emfrail().`

- added a warning for when the limits for searching of the likelihood based confidence interval are reached.
- removed that message with calculating information matrix

- big update comprising all previous changes: many new methods, organized plot methdos, speed improvements.
- updated documentation
- minor bug fixes

- now it’s
`emfrail_dist()`

rather than`emfrail_distribution()`

- a bunch of small fixes and improvements

- added a larger number of methods for
`emfrail`

objects.

- the
`predict.emfrail`

method suffered some alterations: first of all, it now gives predictions for each`lp`

or each row of`newdata`

, and it also gained the argumnet`individual`

. If true, then the`newdata`

argument is taken as coming from the same individual. This can be used with time-dependent covariates and adjusting the time at risk.

- the
`emfrail`

object type has been re-vamped into a more conventional object - cleaned up the code of the methods

- now no more arguments starting with dots and a more conventional
`emfrail(formula, data, stuff)`

phrasing of the main fitting function. - added more checks of the input and warnings that try to tell the user whether the old
`.formula`

or`.data`

arguments are still used.

- removed all the plot functions and replaced them by methods with
`plot.emfrail()`

and`autoplot.emfrail()`

(for`ggplot2`

).

- massive performance improvement, when there are a lot of distinct event time points. moved part of the calculation of the information matrix to c++

- fixed intervals for calculating confidence based intervals. seems there is a problem when the frailty variance is very large (e.g. 30, 40)
- fixed an issue where nlm was not taking the parameters from the .control argument
- set the step size smaller for the nlm maximizer so that it doesn’t overshoot (see 1st issue)

- big overhaul of the
`control`

argument and the`emfrail_control()`

function

- removed some old dependencies in the documentation and DESCRIPTION

- overall, numerous improvements compared to the previous release. Key new features include likelihood based confidence interval for the frailty parameter, more measures of dependence calculated with
`summary()`

, plots using`ggplot2`

, and numerous bug fixes.

- now the call is printed also when the summary is printed

- performance improvements. Now the likelihood-based confidence intervals should take less time as they know better where to look.

- moved from
`optimize`

+`numDeriv`

to`nlm`

- added a number of dependence measures that can be compared across distributions such as Kendall’s tau, median concordance.
- changed quite a lot in the structure of the summary object and the print method to make it more consistent and easier to develop in the future

- added score test for dependent censoring

`ggplot_emfrail()`

added! Now the same plots (and more) can be done with the good looking`ggplot2`

engine.

`summary.emfrail()`

now has a new argument`print_opts`

that is used in`print.emfrail_summary()`

; if the output becomes too big, then some parts of the output may be ommitted

- The optimization now is regulated by search intervals described in the
`emfrail_control()`

and the`.control`

argument. - The parametrization of the stable distribution has been changed, just removed the \(1-\) in the beginning (why did I have that there again?)
- There are different intervals for the gamma/pvf and stable distributions. That’s roughly because the stable chokes with small values of
`theta`

. This should be tuned somehow in the future. The problem lies in the M step where`agreg.fit`

can’t deal with large offset values. - Likelihood confidence based intervals now do the correct thing when the estimate is close to the parameter space but not quite there
- Eliminated the fast fit for the inverse gaussian, this also seems to choke (the fast E step, can’t figure out why), while the slow fit in C++ works fine…
- A slight update in documentation.

TODO: - recover lost features in this update: measures of dependence in `summary.emfrail`

, first of all - bring back the fast fit for inverse gaussian or… who knows, maybe now - document `emfrail_control`

properly - update vignette

Likelihood based confidence intervals are here!

Removed the maximization by `optimx`

and doing it with `optimize()`

, since it’s one dimensional. A hessian estimate is obtained from `numDeriv()`

.

Minor bug fixes

Some big changes in how the confidence intervals are constructed in predict.emfrail. Now - they are first constructed with the delta method for the log(cumulative hazard) and then exponentiated, so they do not have to be truncated at 0 or 1 any more.

Further improved compatibility with CRAN policies and added a bunch of stuff in the examples in `\dontrun`

statements (now they should be less than 5 seconds runtime)

Improved compatibility with R-devel 3.4.0. Registered C++ files to get rid of an R CMD check NOTE. Small modifications in the C++ file - for some reason a segfault started happening out of nowhere, think it’s fixed now.

Added vignette, fixed small things for R CMD check R CMD check: PASS, 0 warnings, 1 note / about new developer, that’s ok.

Added the Commenges-Andersen test for heterogeneity. The test is implemented in a pretty non-efficient way, and it can be skipped with a proper `emfrail_control()`

call, see `?emfrail_control`

. Also there I added an option to *just* calculate the test, instead of doing anything else, and then just that is returned. A nice idea would be to implement this as a post-hoc calculation for `coxph`

objects but that seems like another project atm.

R CMD check: PASS, 0 warnings, 0 notes.

Changed name to the more professional `frailtyEM`

. Added CI and SE for Kendall’s tau with gamma

bugfixes: CI for tau with stable is now ok

Added a `newdata`

option for the `predict`

method and for the `plot`

methods. This can be used instead of `lp`

, and basically calculates the corresponding linear predictor for the given covariate values.

bugfixes

There is an option now to calculate the unadjusted SE or no SE at all

- There are now plot methods available! Check out
`?plot_emfrail`

- Documentation updated accordingly

- Added a
`NEWS.md`

file to track changes to the package.