Merge pull request #46 from TJHeaton/development

Acceptance by CRAN as v1.0.1

CRAN-SUBMISSION

@@ -1,4 +1,3 @@
-Version: 1.0.0
-Date: 2024-01-25 12:00:37 UTC
-SHA:
-    61ef58cf978a4b4ef7dc3f2cb66a4dc5ee7039e8
+Version: 1.0.1
+Date: 2024-01-29 15:41:13 UTC
+SHA: b8e397982597cdc1eef472752ecde7af1e5f250d

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: carbondate
 Title: Calibration and Summarisation of Radiocarbon Dates
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: c(
     person("Timothy J", "Heaton", , "T.Heaton@leeds.ac.uk", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "0000-0002-9994-142X")),

NEWS.md

@@ -1,3 +1,7 @@
+# carbondate 1.0.1
+
+* Fixing CRAN comments 
+
 # carbondate 1.0.0
 
 * First release

README.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/TJHeaton/carbondate/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/TJHeaton/carbondate/actions/workflows/R-CMD-check.yaml)
-![CRAN status](https://www.r-pkg.org/badges/version/carbondate)](https://CRAN.R-project.org/package=carbondate)
+[![CRAN status](https://www.r-pkg.org/badges/version/carbondate)](https://CRAN.R-project.org/package=carbondate)
 <!-- badges: end -->
 
 An R package to analyse, and summarise, multiple radiocarbon (^14^C) determinations. The package provides two linked (but distinct) Bayesian approaches that can both be used to obtain rigorous and robust alternatives to summed probability distributions (SPDs):

README.md

@@ -6,8 +6,8 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/TJHeaton/carbondate/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/TJHeaton/carbondate/actions/workflows/R-CMD-check.yaml)
-![CRAN
-status](https://www.r-pkg.org/badges/version/carbondate)\](<https://CRAN.R-project.org/package=carbondate>)
+[![CRAN
+status](https://www.r-pkg.org/badges/version/carbondate)](https://CRAN.R-project.org/package=carbondate)
 <!-- badges: end -->
 
 An R package to analyse, and summarise, multiple radiocarbon

cran-comments.md

@@ -14,7 +14,9 @@ We have also:
 
 * Altered documentation of functions (and vignettes) to incorporate above plotting changes (and fix a few typos to ensure parameter naming consistency across outputs).
 
-* Fixed typos in vignettes and run MCMC in "Non-parametric-summed-density" for longer to show converged result
+* Fixed typos in vignettes
+
+* Run MCMC in "Non-parametric-summed-density" for longer to show converged result, shortened other vignettes to get under 10min runtime
 
 * Switched antialiaising back to default on png on vignettes.
 

vignettes/Against_SPDs.Rmd

@@ -118,6 +118,7 @@ polya_urn_output <- PolyaUrnBivarDirichlet(
   rc_determinations = two_normals$c14_age,
   rc_sigmas = two_normals$c14_sig,
   calibration_curve=intcal20,
+  n_iter = 1e4,
   show_progress = FALSE)
 
 two_normals_DPMM <- PlotPredictiveCalendarAgeDensity(
@@ -130,7 +131,7 @@ par(new = TRUE,
     yaxs = "i",
     mar = c(5, 4.5, 4, 2) + 0.1,
     las = 1)
-xlim <- rev(range(two_normals_DPMM[[1]]$calendar_age))
+xlim <- rev(range(two_normals_DPMM[[1]]$calendar_age_BP))
 ylim <- c(0, 3 * max(two_normals_DPMM[[1]]$density_mean))
 plot(
     NULL,

vignettes/determining-convergence.Rmd

@@ -34,7 +34,7 @@ To assess convergence of our methods, we apply it to the individual posterior ca
 
 In the first case, the relevant MCMC function can be run multiple times. This generate different chains.
 
-```{r calculate_gr_multiple, results=FALSE}
+```{r calculate_gr_multiple, results=FALSE, eval=FALSE}
 all_outputs <- list()
 for (i in 1:3) {
   set.seed(i + 1)
@@ -43,16 +43,22 @@ for (i in 1:3) {
 }
 PlotGelmanRubinDiagnosticMultiChain(all_outputs)
 ```
+```{r, out.width= "100%", echo = FALSE}
+knitr::include_graphics("figures-convergence/calculate_gr_multiple-1.png")
+```
 
 It can also be calculated by taking a single MCMC run, and splitting it into multiple parts to compare the _within-segment_ variance with the _between-segment_ variance for each calendar age observation. 
 
-```{r calculate_gr, results=FALSE}
+```{r calculate_gr, results=FALSE, eval=FALSE}
 set.seed(3)
 output <- PolyaUrnBivarDirichlet(
     kerr$c14_age, kerr$c14_sig, intcal20, n_iter = 2e4)
 
 PlotGelmanRubinDiagnosticSingleChain(output, n_burn = 5e3)
 ```
+```{r, out.width= "100%", echo = FALSE}
+knitr::include_graphics("figures-convergence/calculate_gr-1.png")
+```
 
 As you can see, even with a few iterations (where we would expect the result not to have converged yet) the PSRF values are close to one. 
 
@@ -66,7 +72,7 @@ Unfortunately, the chains storing these parameters are not suitable for the Gelm
 
 Running the functions a few time with different random number seeds can give an idea of how many iterations are needed for convergence. If the MCMC has converged, then each run should lead to a similar result for the predictive density (or the posterior occurrence rate in the case of the POisson process model). For example,
 
-```{r calculate_polya_kerr, results=FALSE}
+```{r calculate_polya_kerr, results=FALSE, eval=FALSE}
 outputs <- list()
 for (i in 1:3) {
   set.seed(i+1)
@@ -80,12 +86,15 @@ for (i in 1:3) {
 PlotPredictiveCalendarAgeDensity(
   outputs, n_posterior_samples = 500, denscale = 2, interval_width = "1sigma")
 ```
+```{r, out.width= "100%", echo = FALSE}
+knitr::include_graphics("figures-convergence/calculate_polya_kerr-1.png")
+```
 
 As you can see, in this case (255 determinations collated by Kerr and McCormick [@kerr2014]) the different runs do not have similar outputs, so more iterations would be needed to ensure convergence.
 
 In contrast, if we run a much simpler example (that of artificial data comprised of two normals), we can see that convergence appears to be achieved in a small number of iterations.
 
-```{r calculate_polya_normals, results=FALSE}
+```{r calculate_polya_normals, results=FALSE, eval=FALSE}
 outputs <- list()
 for (i in 1:3) {
   set.seed(i + 1)
@@ -99,14 +108,17 @@ for (i in 1:3) {
 PlotPredictiveCalendarAgeDensity(
   outputs, n_posterior_samples = 500, denscale = 2, interval_width = "1sigma")
 ```
+```{r, out.width= "100%", echo = FALSE}
+knitr::include_graphics("figures-convergence/calculate_polya_normals-1.png")
+```
 
 This approach to assessing convergence can be taken with either the Bayesian non-parametric method, or the Poisson process modelling.
 
 ### Examining the Kullback--Leibler divergence (Bayesian non-parametrics only)
 
 We also provide a further diagnostic specifically for the Bayesian non-parametric approach (it is not applicable for the Poisson process model). This diagnostic gives a measure of the difference between an initial (baseline) predictive density and the predictive density as the MCMC progresses.
 
-```{r calculate_kld, results=FALSE}
+```{r calculate_kld, results=FALSE, eval=FALSE}
 set.seed(50)
 output <- WalkerBivarDirichlet(
     rc_determinations = kerr$c14_age,
@@ -116,6 +128,9 @@ output <- WalkerBivarDirichlet(
 
 PlotConvergenceData(output)
 ```
+```{r, out.width= "100%", echo = FALSE}
+knitr::include_graphics("figures-convergence/calculate_kld-1.png")
+```
 
 It can give an idea of convergence as well as which iteration number to use for `n_burn` when calculating the predictive density (by default set to half the chain).