- maintainance update: fixes CRAN issues due to new RNG

- Updates to cummulative effect vignette
- Updates to time-dependent covariate vignette (+ data transformation)
- Update citation information

`concurrent`

now has a`lag = 0`

argument, can be set to positive integer values`as_ped`

accepts multiple`concurrent`

specials with different`lag`

specifications

- Further improved support for cumulative effects
- Added vignette on estimation and visualization of cumulative effect
- Updated vignette on convenience functions (now “Workflow and convenience functions”)
- Other (minor) upgrades/updates to documentation/vignettes
- Updates homepage (via pkgdown)

- Update documentation
- More tests/improved coverage
- Lag-lead column is adjusted in
`make-newdata.fped`

- visualization functions
`gg_laglead`

and`gg_partial_ll`

did not calculate the lag-lead-window correctly when applied to`ped`

data

- Better support for cumulative effects
- Lag-Lead matrix now contains quadrature weights
- Better support for visualization of cumulative effects

`make_newdata`

loses arguments`expand`

and`n`

and gains`...`

where arbitrary covariate specifications can be placed, i.e. e.g.`age=seq_range(age, n=20)`

. Multiple such expression can be provided and a data frame with one row for each combination of the evaluated expressions will be returned. All variables not specified in will be set to respective mean or modus values. For data of class`ped`

or`fped`

`make_newdata`

will try to specify time-dependent variables intelligently.`te_var`

argument in`concurrent`

and`cumulative`

was renamed to`tz_var`

`te`

arguments have been replaced by`tz`

(time points at which`z`

was observed) in all functions to avoid confusion with`mgcv::te`

(e.g.,`gg_laglead`

)

Overall better support for cumulative effects

- Added convenience functions for work with cumulative effects, namely
`gg_partial`

and`gg_slice`

- Added helper functions to calculate and visualize Lag-lead windows
`get_laglead`

`gg_laglead`

- Added convenience
`geom`

s for piece-wise constant hazards (see examples in`?geom_hazard`

, cumulative hazards and survival probabilities (usually`aes(x=time, y = surv_prob)`

, but data set doesn’t contain extra row for`time = 0`

), thus`geom_stephazard`

adds row (x=0, y = y[1]) to the data before plotting`geom_hazard`

adds row (x = 0, y = 0) before plotting (can also be used for cumulative hazard)`geom_surv`

add row (x = 0, y = 1) before plotting

All data transformation is now handled using

`as_ped`

(see data transformation vignette)- Data transformation now handles
- standard time-to-event data
- time-to-event data with concurrent effects of time-dependent covariates
- time-to-event data with cumulative effects of time-dependent covariates

Added functionality to flexibly simulate data from PEXP including cumulative effects, see

`?sim_pexp`

Added functionality to calculate Aaalen-model style cumulative coefficients, see

`?cumulative_coefficient`

- Breaking change in
`split_data`

(`as_ped`

now main data trafo function):- removed
`max.end`

argument - added
`max_time`

argument to introduce administrative censoring at`max_time`

when no custom interval split points are provided

- removed

- More
`tidyeval`

adaptations - consistent handling of “no visible global binding” NOTEs
- Release used in

A. Bender, Groll A., Scheipl F., “A generalized additive model approach to time-to-event analysis” (2017). Statistical Modelling (*to appear*)

- some adaptations to
`tidyeval`

- Minor bug fixes

- Ported
`pamm`

package to`pammtools`

due to naming conflicts with`PAMM`

package on CRAN