Merge pull request #31 from onnela-lab/jss-publication

Update metadata for publication in Journal of Statistical Software.

Author: tillahoffmann

.github/workflows/python.yml

@@ -59,6 +59,7 @@ jobs:
           packages-dir: python/${{ matrix.module }}/dist
           repository-url: https://test.pypi.org/legacy/
           skip-existing: true
+          verbose: true
       - name: Delete attestation file if present to pass twine check
         run: rm -f ${{ matrix.module }}/dist/*.attestation
       - name: Upload the package to pypi on `main` only
@@ -67,3 +68,4 @@ jobs:
         with:
           packages-dir: python/${{ matrix.module }}/dist
           skip-existing: true
+          verbose: true

CITATION.cff

@@ -19,8 +19,11 @@ preferred-citation:
   - family-names: Onnela
     given-names: Jukka-Pekka
     orcid: https://orcid.org/0000-0001-6613-8668
-  doi: 10.48550/arXiv.2301.08836
-  journal: arXiv
-  start: "2301.08836"
+  doi: 10.18637/jss.v112.i02
+  journal: Journal of Statistical Software
+  volume: 112
+  issue: 2
+  start: 1
+  end: 31
   title: Scalable Gaussian Process Inference with Stan
-  year: 2023
+  year: 2025

R/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: gptoolsStan
 Title: Gaussian Processes on Graphs and Lattices in 'Stan'
-Version: 0.2.0
+Version: 1.0.0
 Authors@R: c(
     person("Till", "Hoffmann", , "thoffmann@hsph.harvard.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-4403-0722")),
@@ -12,7 +12,7 @@ Description: Gaussian processes are flexible distributions to model functional d
     sparse approximation of the likelihood that is generally applicable and, second, an exact method
     for regularly spaced data modeled by stationary kernels using fast Fourier methods. Utility
     functions are provided to compile and fit 'Stan' models using the 'cmdstanr' interface.
-    References: Hoffmann and Onnela (2022) <doi:10.48550/arXiv.2301.08836>.
+    References: Hoffmann and Onnela (2025) <doi:10.18637/jss.v112.i02>.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Language: en-US

R/cran-comments.md

@@ -1,3 +1,7 @@
+# Submission of version 1.0.0.
+
+The DOI in the CITATION is for a new JSS publication that will be registered after publication on CRAN.
+
 # Submission of version 0.2.0.
 
 This submission adds functionality to specify different length scales in different dimensions for Gaussian processes on graphs.

R/inst/CITATION

@@ -0,0 +1,17 @@
+bibentry(bibtype = "Article",
+  title        = "{gptools}: Scalable {G}aussian Process Inference with {Stan}",
+  author       = c(person(given = "Till",
+                          family = "Hoffmann",
+                          email = "thoffmann@hsph.harvard.edu"),
+                   person(given = "Jukka-Pekka",
+                          family = "Onnela",
+                          email = "onnela@hsph.harvard.edu")),
+  journal      = "Journal of Statistical Software",
+  year         = "2025",
+  volume       = "112",
+  number       = "2",
+  pages        = "1--31",
+  doi          = "10.18637/jss.v112.i02",
+  header       = "To cite gptoolsStan in publications use:"
+)
+

R/vignettes/getting_started.Rmd

@@ -25,7 +25,7 @@ cmdstanr::install_cmdstan(version = "2.36.0")
 
 `gptoolsStan` is a minimal package to publish Stan code for efficient Gaussian process inference. The package can be used with the [`cmdstanr`](https://mc-stan.org/cmdstanr/) interface for Stan in R. Unfortunately, [`Rstan`](https://mc-stan.org/rstan/) is not supported because it [does not provide an option to specify include paths](https://discourse.mc-stan.org/t/specifying-include-paths-in-rstan/32182/2). If you're already familiar with `cmdstanr`, dive in below. If not, have a look at the [getting started guide](https://mc-stan.org/cmdstanr/articles/cmdstanr.html) for the `cmdstanr` interface.
 
-This vignette demonstrates the package by sampling from a simple Gaussian process using Fourier methods (see the accompanying publication ["Scalable Gaussian Process Inference with Stan"](https://arxiv.org/abs/2301.08836) for background on the approach). Here is the model definition in Stan.
+This vignette demonstrates the package by sampling from a simple Gaussian process using Fourier methods (see the accompanying publication ["Scalable Gaussian Process Inference with Stan"](https://doi.org/10.18637/jss.v112.i02) for background on the approach). Here is the model definition in Stan.
 
 ```{r, results='markup', comment='', echo=FALSE}
 cat(readLines("getting_started.stan"), sep = "\n")
@@ -49,7 +49,7 @@ As indicated in the `data` section of the Stan program, we need to define the nu
 n <- 100
 length_scale <- n / 10
 freq <- 1:(n %/% 2 + 1) - 1
-# See appendix B of https://arxiv.org/abs/2301.08836 for details on the expression.
+# See appendix B of https://doi.org/10.18637/jss.v112.i02 for details on the expression.
 cov_rfft <- exp(- 2 * (pi * freq * length_scale / n) ^ 2) + 1e-9
 ```
 

README.md

@@ -5,7 +5,7 @@ Gaussian processes (GPs) are powerful distributions for modeling functional data
 1. A sparse approximation of the likelihood. This approach includes nearest neighbor Gaussian processes but also supports more general dependence structures, e.g., for periodic kernels.
 2. An exact likelihood evaluation for data on regularly spaced lattices using fast Fourier transforms.
 
-The implementation follows Stan’s design and exposes performant inference through a familiar interface. We provide interfaces in Python and R. See the accompanying publication [*Scalable Gaussian Process Inference with Stan*](https://doi.org/10.48550/arXiv.2301.08836) for details of the implementation. The comprehensive [documentation](http://gptools-stan.readthedocs.io/) includes many [examples](https://gptools-stan.readthedocs.io/docs/examples.html).
+The implementation follows Stan’s design and exposes performant inference through a familiar interface. We provide interfaces in Python and R. See the accompanying publication [*Scalable Gaussian Process Inference with Stan*](https://doi.org/10.18637/jss.v112.i02) for details of the implementation. The comprehensive [documentation](http://gptools-stan.readthedocs.io/) includes many [examples](https://gptools-stan.readthedocs.io/docs/examples.html).
 
 ## Getting Started
 
@@ -72,4 +72,4 @@ Contributions to the package are always welcome! The repository structure is as
 
 ## Replicating the Results
 
-To replicate the results presented in the accompanying publication [*Scalable Gaussian Process Inference with Stan*](https://doi.org/10.48550/arXiv.2301.08836), please see the [dedicated repository of replication materials](https://github.com/onnela-lab/gptools-reproduction-material).
+To replicate the results presented in the accompanying publication [*Scalable Gaussian Process Inference with Stan*](https://doi.org/10.18637/jss.v112.i02), please see the [dedicated repository of replication materials](https://github.com/onnela-lab/gptools-reproduction-material).

index.rst

@@ -26,7 +26,7 @@ Gaussian processes (GPs) are powerful distributions for modeling functional data
 1. A :ref:`sparse-approximation` of the likelihood. This approach includes nearest neighbor Gaussian processes but also supports more general dependence structures, e.g., for periodic kernels.
 2. An exact likelihood evaluation for data on regularly spaced lattices using fast :ref:`Fourier-methods`.
 
-The implementation follows Stan's design and exposes performant inference through a familiar interface. We provide interfaces in Python and R. See the accompanying publication `Scalable Gaussian Process Inference with Stan <https://doi.org/10.48550/arXiv.2301.08836>`__ for details of the implementation.
+The implementation follows Stan's design and exposes performant inference through a familiar interface. We provide interfaces in Python and R. See the accompanying publication `Scalable Gaussian Process Inference with Stan <https://doi.org/10.18637/jss.v112.i02>`__ for details of the implementation.
 
 .. _getting-started:
 
@@ -78,4 +78,4 @@ If you use another *Stan* `interface <https://mc-stan.org/users/interfaces/>`__,
 Reproducing Results From the Accompanying Publication
 -----------------------------------------------------
 
-The `accompanying publication "Scalable Gaussian process inference with Stan" <https://arxiv.org/abs/2301.08836>`__ provides theoretical background and a technical description of the methods. All results and figures can be reproduced by following the instructions in the `repository of reproduction materials <https://github.com/onnela-lab/gptools-reproduction-material>`__.
+The `accompanying publication "Scalable Gaussian process inference with Stan" <https://doi.org/10.18637/jss.v112.i02>`__ provides theoretical background and a technical description of the methods. All results and figures can be reproduced by following the instructions in the `repository of reproduction materials <https://github.com/onnela-lab/gptools-reproduction-material>`__.

python/stan/pyproject.toml

@@ -4,15 +4,34 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "gptools-stan"
-version = "0.2.2"
+version = "1.0.0"
 description = "Gaussian processes on graphs and lattices in Stan."
+license.file = "LICENSE"
 readme = "README.md"
+authors = [
+    {name = "Till Hoffmann"},
+    {name = "Jukka-Pekka Onnela"}
+]
+keywords = ["Gaussian process", "Stan", "Bayesian inference", "graph", "Fourier"]
+classifiers = [
+    "Development Status :: 5 - Production/Stable",
+    "Programming Language :: Python :: 3",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: MIT License",
+    "Topic :: Scientific/Engineering",
+]
 requires-python = ">=3.8"
 dependencies = [
-    "cmdstanpy>=1.0.7",  # Required because of a bug in how complex numbers are handled
+    "cmdstanpy>=1.0.7",  # Required because of a bug in how complex numbers are handled.
     "numpy",
 ]
 
+[project.urls]
+Documentation = "https://gptools-stan.readthedocs.io"
+Repository = "https://github.com/onnela-lab/gptools.git"
+Issues = "https://github.com/onnela-lab/gptools/issues"
+Reference = "https://doi.org/10.18637/jss.v112.i02"
+
 [tool.setuptools]
 packages = {find = {namespaces = true}}
 include-package-data = true