DRlib is a C++ library for modeling, simulation and inference of marked
point processes.
The aim is to complement existing tools, such as the spatstat library in R [1], with reliable and efficient C++ code allowing intensive Bayesian MCMC-based inference for marked Gibbs point processes with interactions. This proposal has its roots in the MPPLIB library developed mainly by [B,C], where exact simulation algorithms for marked point processes have been programmed in C++ to perform
massive simulation studies. This library was developed as part of the research of the authors whose names are in italics in the references. Its long-term goal is to create effective and reusable IT tools.
The aim is to complement existing tools, such as the spatstat library in R [Baddeley et al., 2016], with reliable and efficient C++ code allowing intensive Bayesian MCMC-based inference for marked Gibbs point processes with interactions. This proposal has its roots in the MPPLIB library developed mainly by [van Lieshout and Stoica, 2006], where exact simulation algorithms for marked point processes have been programmed in C++ to perform
massive simulation studies. This library was developed as part of the research of the authors whose names are in italics in the references. Its long-term goal is to create effective and reusable IT tools.
The current version of the library is freely available at the following address https://gitlab.univ-lorraine.fr/labos/iecl/drlib.
## References
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"DRlib: a C++ library for marked Gibbs point processes simulation and inference." 21st Annual Conference of the International Association for Mathematical Geosciences, IAMG 2022, 2022. [[PDF](https://hal.science/hal-04047676v1/file/IAMG_poster_2022.pdf)] | [[HAL](https://hal.science/hal-04047676)]
- R. S. Stoica, M. Deaconu, A. Philippe and L. Hurtado-Gil.
"Shadow Simulated Annealing: a new algorithm for approximate Bayesian inference of Gibbs point processes". In: Spatial Statistics 43 (2021), pp. 1–21.
"Shadow Simulated Annealing: a new algorithm for approximate Bayesian inference of Gibbs point processes". In: Spatial Statistics 43 (2021), pp. 1–21.[[PUB](https://www.sciencedirect.com/science/article/abs/pii/S2211675321000154)]|[[HAL](https://hal.science/hal-02183506)]
- R. S. Stoica, A. Philippe, P. Gregori and J. Mateu.
"ABC Shadow algorithm: a tool for statistical analysis of spatial patterns". In: Statistics and Computing 27, (2017), pp. 1225–1238.
"ABC Shadow algorithm: a tool for statistical analysis of spatial patterns". In: Statistics and Computing 27, (2017), pp. 1225–1238.[[PUB](https://link.springer.com/article/10.1007/s11222-016-9682-x)]|[[ARXIV](https://arxiv.org/abs/1507.04228)]
- A. J. Baddeley, E. Rubak and R. Turner.
"Spatial Point Patterns: Methodology and Applications with R." London: Chapman and Hall/CRC Press, 2016.
"Spatial Point Patterns: Methodology and Applications with R." London: Chapman and Hall/CRC Press, 2016.[[PUB](https://www.sciencedirect.com/science/article/abs/pii/S0167947306000703)]|[[CWI](https://ir.cwi.nl/pub/4159)]
- M. N. M. van Lieshout and R. S. Stoica.
"Perfect simulation for marked point processes". In: Computational Statistics and Data Analysis 51 (2006), pp. 679–698.
"Perfect simulation for marked point processes". In: Computational Statistics and Data Analysis 51 (2006), pp. 679–698.[[BOOK](https://www.routledge.com/Spatial-Point-Patterns-Methodology-and-Applications-with-R/Baddeley-Rubak-Turner/p/book/9781482210200)]|[[BOOK](https://book.spatstat.org/)]
## To start quickly and test
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## Authors and acknowledgments
Authors : Didier Gemmerlé, Radu Stefan Stoica, Chistophe Reype and Nathan Gillot.
Contributor : Pierre Auburtin and Diego Astaburuaga.
Contributors : Pierre Auburtin and Diego Astaburuaga.
