From 7b4a4dc9db43a98298c1a95fb512d63e18c3a3ae Mon Sep 17 00:00:00 2001
From: Didier Gemmerle <Didier.Gemmerle@univ-lorraine.fr>
Date: Thu, 23 Jan 2025 15:09:46 +0100
Subject: [PATCH] README update
---
README.md | 15 ++++++++-------
1 file changed, 8 insertions(+), 7 deletions(-)
diff --git a/README.md b/README.md
index e19ac6f..0ee5554 100644
--- a/README.md
+++ b/README.md
@@ -3,11 +3,12 @@
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
@@ -34,16 +35,16 @@ The current version of the library is freely available at the following address
"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
@@ -108,5 +109,5 @@ The documentation is currently being written, and a link will be added when a ve
## 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.
--
GitLab