remove codecov as it keeps hanging indefinitely; update readme

README.Rmd

@@ -26,9 +26,8 @@ knitr::opts_chunk$set(
 > **M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
 
 [![R-CMD-check](https://github.com/nicholasjclark/mvgam/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nicholasjclark/mvgam/actions/)
-[![Test status](https://github.com/nicholasjclark/mvgam/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/nicholasjclark/mvgam/actions/workflows/test-coverage.yaml)
 [![Coverage status](https://codecov.io/gh/nicholasjclark/mvgam/graph/badge.svg?token=RCJ2B7S0BL)](https://app.codecov.io/gh/nicholasjclark/mvgam)
-[![Documentation](https://img.shields.io/badge/documentation-mvgam-orange.svg?colorB=E91E63)](https://nicholasjclark.github.io/mvgam/)
+[![Documentation](https://img.shields.io/badge/documentation-mvgam-orange.svg?colorB=brightgreen)](https://nicholasjclark.github.io/mvgam/)
 [![CRAN Version](https://www.r-pkg.org/badges/version/mvgam)](https://cran.r-project.org/package=mvgam)
 [![CRAN Downloads](https://cranlogs.r-pkg.org/badges/grand-total/mvgam?color=brightgreen)](https://cran.r-project.org/package=mvgam)
 
@@ -56,14 +55,11 @@ When using any software please make sure to appropriately acknowledge the hard w
 
 When using `mvgam`, please cite the following:
 
-- Clark, N.J. and Wells, K. (2022). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. *Methods in Ecology and Evolution*. DOI: https://doi.org/10.1111/2041-210X.13974
+> Clark, N.J. and Wells, K. (2022). Dynamic Generalized Additive Models (DGAMs) for forecasting discrete ecological time series. *Methods in Ecology and Evolution*. DOI: https://doi.org/10.1111/2041-210X.13974
 
 As `mvgam` acts as an interface to `Stan`, please additionally cite:
 
-- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B., Betancourt M.,
-  Brubaker M., Guo J., Li P., and Riddell A. (2017). Stan: A probabilistic
-  programming language. *Journal of Statistical Software*. 76(1).
-  10.18637/jss.v076.i01
+> Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B., Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017). Stan: A probabilistic programming language. *Journal of Statistical Software*. 76(1). DOI: https://doi.org/10.18637/jss.v076.i01
 
 `mvgam` relies on several other `R` packages and, of course, on `R` itself. To
 find out how to cite R and its packages, use the `citation` function. There are
@@ -102,7 +98,7 @@ plot(stl(ts(lynx_full$population, frequency = 19), s.window = 'periodic'),
 lynx_full$season <- (lynx_full$year%%19) + 1
 ```
 
-For `mvgam` models, we need an indicator of the series name as a `factor` (if the column `series` is missing, this will be added automatically by assuming that all observations are from a single time series). A `time` column is needed to index time
+For most `mvgam` models, we need an indicator of the series name as a `factor`. A `time` column is also needed for most models to index time (but note that these variables are not necessarily needed for other models supported by `mvgam`, such as [Joint Species Distribution Models](https://nicholasjclark.github.io/mvgam/reference/jsdgam.html))
 ```{r}
 lynx_full$time <- 1:NROW(lynx_full)
 lynx_full$series <- factor('series1')

README.md

@@ -8,11 +8,9 @@
 > **M**ulti**V**ariate (Dynamic) **G**eneralized **A**ddivite **M**odels
 
 [![R-CMD-check](https://github.com/nicholasjclark/mvgam/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nicholasjclark/mvgam/actions/)
-[![Test
-status](https://github.com/nicholasjclark/mvgam/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/nicholasjclark/mvgam/actions/workflows/test-coverage.yaml)
 [![Coverage
 status](https://codecov.io/gh/nicholasjclark/mvgam/graph/badge.svg?token=RCJ2B7S0BL)](https://app.codecov.io/gh/nicholasjclark/mvgam)
-[![Documentation](https://img.shields.io/badge/documentation-mvgam-orange.svg?colorB=E91E63)](https://nicholasjclark.github.io/mvgam/)
+[![Documentation](https://img.shields.io/badge/documentation-mvgam-orange.svg?colorB=brightgreen)](https://nicholasjclark.github.io/mvgam/)
 [![CRAN
 Version](https://www.r-pkg.org/badges/version/mvgam)](https://cran.r-project.org/package=mvgam)
 [![CRAN
@@ -98,16 +96,16 @@ that you rely on for your research.
 
 When using `mvgam`, please cite the following:
 
-- Clark, N.J. and Wells, K. (2022). Dynamic Generalized Additive Models
-  (DGAMs) for forecasting discrete ecological time series. *Methods in
-  Ecology and Evolution*. DOI: <https://doi.org/10.1111/2041-210X.13974>
+> Clark, N.J. and Wells, K. (2022). Dynamic Generalized Additive Models
+> (DGAMs) for forecasting discrete ecological time series. *Methods in
+> Ecology and Evolution*. DOI: <https://doi.org/10.1111/2041-210X.13974>
 
 As `mvgam` acts as an interface to `Stan`, please additionally cite:
 
-- Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,
-  Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).
-  Stan: A probabilistic programming language. *Journal of Statistical
-  Software*. 76(1). 10.18637/jss.v076.i01
+> Carpenter B., Gelman A., Hoffman M. D., Lee D., Goodrich B.,
+> Betancourt M., Brubaker M., Guo J., Li P., and Riddell A. (2017).
+> Stan: A probabilistic programming language. *Journal of Statistical
+> Software*. 76(1). DOI: <https://doi.org/10.18637/jss.v076.i01>
 
 `mvgam` relies on several other `R` packages and, of course, on `R`
 itself. To find out how to cite R and its packages, use the `citation`
@@ -164,10 +162,11 @@ plot(stl(ts(lynx_full$population, frequency = 19), s.window = 'periodic'),
 lynx_full$season <- (lynx_full$year%%19) + 1
 ```
 
-For `mvgam` models, we need an indicator of the series name as a
-`factor` (if the column `series` is missing, this will be added
-automatically by assuming that all observations are from a single time
-series). A `time` column is needed to index time
+For most `mvgam` models, we need an indicator of the series name as a
+`factor`. A `time` column is also needed for most models to index time
+(but note that these variables are not necessarily needed for other
+models supported by `mvgam`, such as [Joint Species Distribution
+Models](https://nicholasjclark.github.io/mvgam/reference/jsdgam.html))
 
 ``` r
 lynx_full$time <- 1:NROW(lynx_full)
@@ -238,28 +237,28 @@ summary(lynx_mvgam)
 #> 
 #> GAM coefficient (beta) estimates:
 #>                2.5%   50%  97.5% Rhat n_eff
-#> (Intercept)   6.400  6.60  6.900 1.00   821
-#> s(season).1  -0.640 -0.12  0.390 1.00  1356
-#> s(season).2   0.780  1.30  1.900 1.00  1241
-#> s(season).3   1.300  1.90  2.500 1.00   889
-#> s(season).4  -0.062  0.53  1.200 1.00  1119
-#> s(season).5  -1.300 -0.72 -0.099 1.01  1113
-#> s(season).6  -1.300 -0.57  0.092 1.00  1101
-#> s(season).7   0.023  0.70  1.400 1.00  1355
-#> s(season).8   0.640  1.40  2.100 1.01  1111
-#> s(season).9  -0.370  0.21  0.830 1.01   847
-#> s(season).10 -1.400 -0.87 -0.370 1.00  1108
+#> (Intercept)   6.400  6.60  6.900    1   660
+#> s(season).1  -0.640 -0.12  0.390    1  1035
+#> s(season).2   0.690  1.30  1.900    1   893
+#> s(season).3   1.200  1.90  2.500    1   842
+#> s(season).4  -0.079  0.53  1.100    1   830
+#> s(season).5  -1.300 -0.71 -0.058    1   849
+#> s(season).6  -1.200 -0.56  0.110    1  1168
+#> s(season).7   0.058  0.72  1.400    1   997
+#> s(season).8   0.660  1.40  2.100    1  1019
+#> s(season).9  -0.350  0.24  0.840    1   906
+#> s(season).10 -1.400 -0.85 -0.370    1  1124
 #> 
 #> Approximate significance of GAM smooths:
 #>            edf Ref.df Chi.sq p-value    
-#> s(season) 9.97     10   48.8  <2e-16 ***
+#> s(season) 9.99     10   45.6  <2e-16 ***
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
 #> Latent trend parameter AR estimates:
 #>          2.5%  50% 97.5% Rhat n_eff
-#> ar1[1]   0.61 0.83  0.99 1.01   666
-#> sigma[1] 0.38 0.48  0.60 1.00   688
+#> ar1[1]   0.59 0.82  0.98 1.00   660
+#> sigma[1] 0.39 0.48  0.61 1.01   628
 #> 
 #> Stan MCMC diagnostics:
 #> n_eff / iter looks reasonable for all parameters
@@ -268,7 +267,7 @@ summary(lynx_mvgam)
 #> 0 of 2000 iterations saturated the maximum tree depth of 12 (0%)
 #> E-FMI indicated no pathological behavior
 #> 
-#> Samples were drawn using NUTS(diag_e) at Thu Oct 24 1:32:39 PM 2024.
+#> Samples were drawn using NUTS(diag_e) at Thu Oct 31 9:16:35 AM 2024.
 #> For each parameter, n_eff is a crude measure of effective sample size,
 #> and Rhat is the potential scale reduction factor on split MCMC chains
 #> (at convergence, Rhat = 1)
@@ -405,7 +404,7 @@ series (testing and training)
 ``` r
 plot(lynx_mvgam, type = 'forecast', newdata = lynx_test)
 #> Out of sample DRPS:
-#> 2470.51814475
+#> 2380.3470865
 ```
 
 <img src="man/figures/README-unnamed-chunk-21-1.png" alt="Plotting forecast distributions using mvgam in R" width="60%" style="display: block; margin: auto;" />
@@ -566,7 +565,7 @@ summary(mod, include_betas = FALSE)
 #> 0 of 2000 iterations saturated the maximum tree depth of 12 (0%)
 #> E-FMI indicated no pathological behavior
 #> 
-#> Samples were drawn using NUTS(diag_e) at Thu Oct 24 1:33:28 PM 2024.
+#> Samples were drawn using NUTS(diag_e) at Thu Oct 31 9:17:20 AM 2024.
 #> For each parameter, n_eff is a crude measure of effective sample size,
 #> and Rhat is the potential scale reduction factor on split MCMC chains
 #> (at convergence, Rhat = 1)