add hex logo

README.Rmd

@@ -18,6 +18,8 @@ knitr::opts_chunk$set(
 )
 ```
 
+<img src="man/figures/mvgam_logo.png" width = 120 alt="brms Logo"/>[<img src="https://raw.githubusercontent.com/stan-dev/logos/master/logo_tm.png" align="right" width=120 alt="Stan Logo"/>](https://mc-stan.org/)
+
 *mvgam*
 ================
 

README.md

@@ -1,6 +1,8 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
+<img src="man/figures/mvgam_logo.png" width = 120 alt="brms Logo"/>[<img src="https://raw.githubusercontent.com/stan-dev/logos/master/logo_tm.png" align="right" width=120 alt="Stan Logo"/>](https://mc-stan.org/)
+
 # *mvgam*
 
 The goal of `mvgam` is to fit Bayesian Dynamic Generalized Additive
@@ -317,29 +319,29 @@ summary(lynx_mvgam)
 #> 
 #> 
 #> GAM coefficient (beta) estimates:
-#>                2.5%      50%  97.5% Rhat n_eff
-#> (Intercept)   6.100  6.60000 7.0000 1.00   659
-#> s(season).1  -0.630  0.02900 0.7500 1.00  1162
-#> s(season).2  -0.250  0.81000 1.8000 1.01   422
-#> s(season).3  -0.039  1.20000 2.5000 1.01   371
-#> s(season).4  -0.560  0.43000 1.4000 1.01   902
-#> s(season).5  -1.200 -0.14000 1.0000 1.01   565
-#> s(season).6  -1.100  0.00089 1.1000 1.01   703
-#> s(season).7  -0.810  0.37000 1.5000 1.00   873
-#> s(season).8  -0.980  0.26000 1.8000 1.01   366
-#> s(season).9  -1.100 -0.29000 0.6600 1.00   474
-#> s(season).10 -1.400 -0.67000 0.0049 1.00   673
+#>               2.5%     50%   97.5% Rhat n_eff
+#> (Intercept)   6.10  6.6000  7.0000 1.00   771
+#> s(season).1  -0.59  0.0480  0.7200 1.00   973
+#> s(season).2  -0.25  0.8000  1.8000 1.01   389
+#> s(season).3  -0.12  1.2000  2.5000 1.01   321
+#> s(season).4  -0.55  0.4100  1.3000 1.00   892
+#> s(season).5  -1.20 -0.1000  0.9300 1.00   479
+#> s(season).6  -1.00 -0.0089  1.1000 1.00   577
+#> s(season).7  -0.72  0.3400  1.4000 1.01   659
+#> s(season).8  -0.95  0.2200  1.8000 1.01   328
+#> s(season).9  -1.10 -0.3100  0.6700 1.00   398
+#> s(season).10 -1.30 -0.6700 -0.0054 1.01   595
 #> 
 #> Approximate significance of GAM observation smooths:
 #>            edf Chi.sq p-value
-#> s(season) 3.71  18666    0.28
+#> s(season) 4.44  17999    0.27
 #> 
 #> Latent trend AR parameter estimates:
 #>           2.5%   50% 97.5% Rhat n_eff
-#> ar1[1]    0.75  1.10 1.400 1.00   614
-#> ar2[1]   -0.84 -0.41 0.042 1.00  1688
-#> ar3[1]   -0.48 -0.12 0.300 1.01   571
-#> sigma[1]  0.40  0.50 0.640 1.00   868
+#> ar1[1]    0.73  1.10 1.400 1.00   609
+#> ar2[1]   -0.82 -0.41 0.043 1.00  1562
+#> ar3[1]   -0.46 -0.12 0.300 1.01   510
+#> sigma[1]  0.40  0.50 0.630 1.00  1051
 #> 
 #> Stan MCMC diagnostics:
 #> n_eff / iter looks reasonable for all parameters
@@ -348,7 +350,7 @@ 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 Fri Mar 01 9:45:36 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Tue Mar 12 1:50:01 PM 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)
@@ -453,7 +455,7 @@ plotted on the outcome scale, for example:
 ``` r
 require(ggplot2)
 #> Loading required package: ggplot2
-#> Warning: package 'ggplot2' was built under R version 4.3.2
+#> Warning: package 'ggplot2' was built under R version 4.3.3
 plot_predictions(lynx_mvgam, condition = 'season', points = 0.5) +
   theme_classic()
 ```
@@ -470,7 +472,7 @@ plot(lynx_mvgam, type = 'forecast', newdata = lynx_test)
 <img src="man/figures/README-unnamed-chunk-21-1.png" width="60%" style="display: block; margin: auto;" />
 
     #> Out of sample CRPS:
-    #> [1] 2901.021
+    #> [1] 2852.181
 
 And the estimated latent trend component, again using the more flexible
 `plot_mvgam_...()` option to show first derivatives of the estimated
@@ -626,7 +628,7 @@ 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 Fri Mar 01 9:46:54 AM 2024.
+#> Samples were drawn using NUTS(diag_e) at Tue Mar 12 1:51:32 PM 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)