Merge pull request #159 from aseyboldt/joss_edits

JOSS: Add self as author and mention rust interface

paper/paper.md

@@ -5,6 +5,7 @@ tags:
   - Python
   - Julia
   - R
+  - Rust
   - C
   - C++
   - automatic differentiation
@@ -22,16 +23,21 @@ authors:
     orcid: 0000-0002-2433-9688
     equal-contrib: true
     affiliation: 2
+  - name: Adrian Seyboldt
+    orcid: 0000-0002-4239-4541
+    affiliation: 3
   - name: Seth D. Axen
     orcid: 0000-0003-3933-8247
-    affiliation: 3
+    affiliation: 4
 affiliations:
  - name: California State University, Chico
    index: 1
  - name: Center for Computational Mathematics, Flatiron Institute
    index: 2
- - name: "Cluster of Excellence Machine Learning: New Perspectives for Science, University of Tübingen"
+ - name: PyMC Labs
    index: 3
+ - name: "Cluster of Excellence Machine Learning: New Perspectives for Science, University of Tübingen"
+   index: 4
 date: 5 January 2022
 bibliography: paper.bib
 ---
@@ -45,7 +51,7 @@ unconstrained posterior log densities, gradients, and Hessians as well as
 constraining/unconstraining transforms.  Implementation is provided through
 automatic differentiation in the Stan math library
 [@Carpenter:2015]. `BridgeStan` provides in-memory access to the methods of Stan
-models through Python, Julia, and R. This allows algorithm development in these
+models through Python, Julia, R, and Rust. This allows algorithm development in these
 languages with the numerical efficiency and expressiveness of Stan models.
 Furthermore, these features are exposed through a language-agnostic C API,
 allowing foreign function interfaces in other languages to utilize `BridgeStan`
@@ -87,7 +93,7 @@ implementations of statistical algorithms, allowing for more fair comparisons.
 
 `BridgeStan` is an interface, written in C-compatible C++, between a Stan
 program and any higher level language that exposes a C foreign function
-interface. Julia, Python, and R each have C foreign function interfaces. Using
+interface. Julia, Python, R, and Rust each have C foreign function interfaces. Using
 memory allocated within such higher level languages, `BridgeStan` provides
 computations of the log joint density function and corresponding gradient of a Stan
 model, which is itself implemented using highly templated C++ from the Stan math
@@ -127,8 +133,8 @@ parallel applications. They also support all of the internal
 parallelization of Stan models, such as internal parallel map
 functions and GPU-enabled matrix operations.
 
-`BridgeStan` enables memory allocated in the host language (Julia, Python, or
-R), to be reused within Stan, though any language with a C foreign function
+`BridgeStan` enables memory allocated in the host language (Julia, Python, R, or Rust),
+to be reused within Stan, though any language with a C foreign function
 interface could be similarly interfaced to access Stan methods. For instance,
 the `BridgeStan` function `log_density_gradient` has as an optional output argument the
 array into which the gradient will be stored.  By avoiding unnecessary copies,