README.Rmd

---
output: github_document
---

```{r options, echo = FALSE}
library(knitr)
opts_chunk$set(warning = FALSE)
```

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# **`pammtools`**: Piece-Wise Exponential Additive Mixed Modeling Tools


### Installation

Install from CRAN or GitHub using:

```{r eval = FALSE}
# CRAN
install.packages("pammtools")
```

### Overview
**`pammtools`** facilitates the estimation of Piece-wise exponential Additive Mixed Models (PAMMs) for time-to-event data. PAMMs can be represented as generalized additive models and can therefore be estimated using GAM software (e.g. **`mgcv`**), which, compared to other packages for survival analysis, often offers more flexibility w.r.t. to the specification of covariate effects (e.g. non-linear, time-varying effects, cumulative effects, etc.).

To get started, see the [Articles](https://adibender.github.io/pammtools/articles/) section.


<!-- An overview over the packages functionality is given in

- Andreas Bender and Fabian Scheipl, "pammtools: Piece-wise exponential
Additive Mixed Modeling tools", arXiv eprint, 2018, https://arxiv.org/abs/1806.01042


For a tutorial-like introduction to PAMMs see:

  - Andreas Bender, Andreas Groll, and Fabian Scheipl, “A Generalized Additive Model Approach to Time-to-Event Analysis.” Statistical Modelling. https://doi.org/10.1177/1471082X17748083.


A general framework for the representation and estimation of cumulative effects
(or exposure-lag-response associations) is described in:

- Andreas Bender, Fabian Scheipl, Wolfgang Hartl, Andrew G Day, Helmut Küchenhoff, "Penalized estimation of complex, non-linear exposure-lag-response associations", Biostatistics, , kxy003, 2018, https://doi.org/10.1093/biostatistics/kxy003
 -->