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ppdiag

ppdiag: Diagnostic Tools for Temporal Point Processes - Published in JOSS (2021)

https://github.com/owenward/ppdiag

Science Score: 95.0%

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Created over 5 years ago · Last pushed about 3 years ago
Metadata Files
Readme Changelog Contributing License Code of conduct

README.Rmd 

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "75%"
)
```

# ppdiag


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[![DOI](https://joss.theoj.org/papers/10.21105/joss.03133/status.svg)](https://doi.org/10.21105/joss.03133)



``ppdiag`` is an `R` package which provides a collection of tools which
can be used to assess the fit of temporal point processes to data.

These currently include:

- Simulating data from a specified point process
- Fitting a specified point process model to data
- Evaluating the fit of a point process model to data using 
several diagnostic tools

# Installation

You can install the released version of ppdiag from [CRAN](https://CRAN.R-project.org) with:

```{r, eval=FALSE}
install.packages("ppdiag")
```

The current development version of this
package is available from [GitHub](https://github.com/OwenWard/ppdiag) with:

```{r, eval=FALSE}
# install.packages("remotes")
remotes::install_github("OwenWard/ppdiag")
```
# Example

To illustrate some of the basic functionality of this package,
we can simulate data from a specified Hawkes process and examine
our diagnostic results when we fit a homogeneous Poisson process
to this data.

```{r example}
library(ppdiag)

hp_obj <- pp_hp(lambda0 = 0.2, alpha = 0.35, beta = 0.8)
sim_hp <- pp_simulate(hp_obj, end = 200)
sim_hp

```
We can readily evaluate the fit of a homogeneous Poisson process to this
data.

```{r fit_hpp}
est_hpp <- fithpp(sim_hp)
est_hpp

pp_diag(est_hpp, events = sim_hp)
```


```{r fit_hp}
hp_est <- fithp(events = sim_hp)
pp_diag(hp_est, events = sim_hp)

```


## Markov Modulated Hawkes Process Example

This is particularly useful for more complex point processes, such as the 
Markov Modulated Hawkes Process (MMHP).
We can simulate events from this model
and examine the fit of simpler point processes to this data.

```{r mmhp example}
Q <- matrix(c(-0.2, 0.2, 0.1, -0.1), ncol = 2, byrow = TRUE)

mmhp_obj <- pp_mmhp(Q, delta = c(1 / 3, 2 / 3), 
          lambda0 = 0.2,
          lambda1 = .75,
          alpha = 0.4,
          beta = 0.8)

mmhp_obj
mmhp_events <- pp_simulate(mmhp_obj, n = 50)
```

We can easily fit a homogeneous Poisson process and visualise the goodness of 
fit.

```{r fit_hpp_to_mmhp}
est_hpp <- fithpp(events = mmhp_events$events)
pp_diag(est_hpp,mmhp_events$events)
```

Similarly for a Hawkes process.

```{r fit_hp_to_mmhp}
est_hp <- fithp(events = mmhp_events$events)
pp_diag(est_hp,mmhp_events$events)
```

We can then compare to the true point process model.

```{r fit_mmhp}
pp_diag(mmhp_obj, mmhp_events$events)
```


# Getting help and contributing

Please file any issues [here](https://github.com/OwenWard/ppdiag/issues). 
Similarly, we would be delighted if anyone would like to contribute to
this package (such as adding other point processes, kernel functions). 
Feel free to take a look
[here](https://github.com/OwenWard/ppdiag/blob/main/CONTRIBUTING.md)
and reach out with any questions.

# References

- Sun et al., (2021). ppdiag: Diagnostic Tools for Temporal Point Processes. Journal of Open Source Software, 6(61), 3133, https://doi.org/10.21105/joss.03133
- Wu et al. (2021), Diagnostics and Visualization of Point Process Models for Event Times on a Social Network, 
In Applied Modeling Techniques and Data Analysis 1 (eds Y. Dimotikalis, A. Karagrigoriou, C. Parpoula and C.H. Skiadas).
https://doi.org/10.1002/9781119821588.ch7

Owner

  • Name: Owen Ward
  • Login: OwenWard
  • Kind: user
  • Location: Vancouver/Dublin

Assistant Professor, Statistics and Actuarial Science, Simon Fraser University

JOSS Publication

ppdiag: Diagnostic Tools for Temporal Point Processes
Published
May 27, 2021
DOI
Volume 6, Issue 61, Page 3133
Authors
Sally Sun
Department of Statistics, Columbia University
Owen G. Ward ORCID
Department of Statistics, Columbia University
Jing Wu
Department of Statistics, Columbia University
Lihao Xiao
Department of Statistics, Columbia University
Xiaoxi Zhao
Department of Statistics, Columbia University
Tian Zheng
Department of Statistics, Columbia University, Data Science Institute, Columbia University
Editor
Mikkel Meyer Andersen ORCID
Tags
Diagnostics Goodness of fit Temporal Point Processes
View PDF Review Thread Software Archive

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Packages

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cran.r-project.org: ppdiag

Diagnosis and Visualizations Tools for Temporal Point Processes

  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 193 Last month
Rankings
Forks count: 17.8%
Stargazers count: 26.2%
Dependent packages count: 29.8%
Dependent repos count: 35.5%
Average: 38.4%
Downloads: 82.8%
Maintainers (1)
owen.ward@columbia.edu
Last synced: 6 months ago

Dependencies

DESCRIPTION cran
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