New release with R-Journal pub.

Author: mhahsler

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: pomdp
 Title: Infrastructure for Partially Observable Markov Decision Processes (POMDP)
-Version: 1.2.4
-Date: 2024-12-05
+Version: 1.2.5
+Date: 2025-05-29
 Authors@R: c(
     person(given = "Michael",
        family = "Hahsler",
@@ -11,7 +11,7 @@ Authors@R: c(
     person(given = "Hossein",
        family = "Kamalzadeh",
        role = c("ctb")))
-Description: Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Smallwood and Sondik (1973) <doi:10.1287/opre.21.5.1071>.
+Description: Provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.
 Classification/ACM: G.4, G.1.6, I.2.6
 URL: https://github.com/mhahsler/pomdp
 BugReports: https://github.com/mhahsler/pomdp/issues

NEWS.md

@@ -1,3 +1,9 @@
+# pomdp 1.2.5 (05/29/2025)
+
+## Changes
+* Added source data to GitHub
+* Added reference to the R-Journal article. 
+
 # pomdp 1.2.4 (12/04/2024)
 
 ## New Features

README.Rmd

@@ -15,9 +15,9 @@ A partially observable Markov decision process (POMDP) models an agent decision
 where the agent cannot directly observe the environment's state, but has to rely on 
 observations. The goal is to find an optimal policy to guide the agent's actions.
 
-The `pomdp` package provides the infrastructure to define and analyze the solutions 
+The `pomdp` package [@Hahsler2025] provides the infrastructure to define and analyze the solutions 
 of optimal control problems formulated as Partially Observable Markov Decision Processes (POMDP). 
-The package uses the solvers from [pomdp-solve](http://www.pomdp.org/code/) (Cassandra, 2015) 
+The package uses the solvers from [pomdp-solve](http://www.pomdp.org/code/) [@Cassandra2015] 
 available in the companion R package [**pomdpSolve**](https://github.com/mhahsler/pomdpSolve) to solve 
 POMDPs using a variety of exact and approximate algorithms.
 
@@ -42,7 +42,7 @@ to the following algorithms:
 If you are new to POMDPs then start with the [POMDP Tutorial](https://pomdp.org/tutorial/).
 
 ```{r echo=FALSE, results = 'asis'}
-pkg_citation(pkg, 1)
+pkg_citation(pkg, 2)
 pkg_install(pkg)
 ```
 

README.md

@@ -15,11 +15,12 @@ decision process where the agent cannot directly observe the
 environment’s state, but has to rely on observations. The goal is to
 find an optimal policy to guide the agent’s actions.
 
-The `pomdp` package provides the infrastructure to define and analyze
-the solutions of optimal control problems formulated as Partially
-Observable Markov Decision Processes (POMDP). The package uses the
-solvers from [pomdp-solve](http://www.pomdp.org/code/) (Cassandra, 2015)
-available in the companion R package
+The `pomdp` package (Hahsler and Cassandra 2025) provides the
+infrastructure to define and analyze the solutions of optimal control
+problems formulated as Partially Observable Markov Decision Processes
+(POMDP). The package uses the solvers from
+[pomdp-solve](http://www.pomdp.org/code/) (Cassandra 2015) available in
+the companion R package
 [**pomdpSolve**](https://github.com/mhahsler/pomdpSolve) to solve POMDPs
 using a variety of exact and approximate algorithms.
 
@@ -50,16 +51,21 @@ Tutorial](https://pomdp.org/tutorial/).
 
 To cite package ‘pomdp’ in publications use:
 
-> Hahsler M (2024). *pomdp: Infrastructure for Partially Observable
-> Markov Decision Processes (POMDP)*. R package version 1.2.4,
-> <https://github.com/mhahsler/pomdp>.
-
-    @Manual{,
-      title = {pomdp: Infrastructure for Partially Observable Markov Decision Processes (POMDP)},
-      author = {Michael Hahsler},
-      year = {2024},
-      note = {R package version 1.2.4},
-      url = {https://github.com/mhahsler/pomdp},
+> Hahsler M, Cassandra AR (2025). “Pomdp: A computational infrastructure
+> for partially observable Markov decision processes.” *The R Journal*,
+> *16*(2), 1-18. ISSN 2073-4859, <doi:10.32614/RJ-2024-021>
+> <https://doi.org/10.32614/RJ-2024-021>.
+
+    @Article{,
+      title = {Pomdp: A computational infrastructure for partially observable Markov decision processes},
+      author = {Michael Hahsler and Anthony R. Cassandra},
+      year = {2025},
+      journal = {The R Journal},
+      volume = {16},
+      number = {2},
+      pages = {1--18},
+      doi = {10.32614/RJ-2024-021},
+      issn = {2073-4859},
     }
 
 ## Installation
@@ -163,6 +169,15 @@ Thirteenth Conference on Uncertainty in Artificial Intelligence*,
 
 </div>
 
+<div id="ref-Hahsler2025" class="csl-entry">
+
+Hahsler, Michael, and Anthony R. Cassandra. 2025. “Pomdp: A
+Computational Infrastructure for Partially Observable Markov Decision
+Processes.” *The R Journal* 16 (2): 116–33.
+<https://doi.org/10.32614/RJ-2024-021>.
+
+</div>
+
 <div id="ref-Kurniawati2008" class="csl-entry">
 
 Kurniawati, Hanna, David Hsu, and Wee Sun Lee. 2008. “SARSOP: Efficient

inst/CITATION

@@ -1 +1,22 @@
 citation(auto = meta)
+
+bibentry(bibtype = "article",
+  title = paste("Pomdp: A computational infrastructure for partially",
+                "observable Markov decision processes"),
+  author = {
+    c(person("Michael", "Hahsler",
+      email = "mhahsler@lyle.smu.edu",
+      comment = c(ORCID = "0000-0003-2716-1405")
+      ),
+      person(c("Anthony", "R."), "Cassandra",
+        email = "cassandra@gmail.com")
+     )
+  },
+  year = 2025,
+  journal = "The R Journal",
+  volume = 16,
+  number = 2,
+  pages = "116--133",
+  doi = "10.32614/RJ-2024-021",
+  issn = "2073-4859"
+  )

inst/README_files/policy_graph-1.png

@@ -27,7 +27,7 @@ library(pomdp)
 
 Gridworlds represent an easy to explore how Markov Decision Problems (MDPs),
 Partially Observable Decision Problems (POMDPs), and various approaches to solve these problems
-work. The R package **pomdp** [@Hahsler2024] provides a set of helper functions starting with the 
+work. The R package **pomdp** [@Hahsler2025],[@Hahsler2024] provides a set of helper functions starting with the 
 prefix `gridworld_`
 to make defining and experimenting with gridworlds easy. 
 

man/pomdp-package.Rd

@@ -24,7 +24,7 @@ library("pomdp")
 
 # Introduction
 
-The R package **pomdp** [@Hahsler2024] provides the infrastructure to define and
+The R package **pomdp** [@Hahsler2025],[@Hahsler2024] provides the infrastructure to define and
 analyze the solutions of Partially Observable Markov Decision Processes
 (POMDP) models. The package is a companion to package **pomdpSolve**
 which provides the executable for