more tests

README.Rmd

@@ -31,7 +31,7 @@ _Accelerate Bayesian analytics workflows_ in R through interactive modelling, vi
 > All introductory Bayesian models and many advanced models are well-supported in the `causact` package; using multi-variate distributions like the multi-variate normal, multi-nomial, or dirichlet distribution are harder to implement as is using directed acyclic graphs with nested or overlapping plates. Improvements to model construction for those more complex models is forthcoming.
 
 ```{r demoGif, out.width = "40%", echo = FALSE, fig.align = "center"}
-knitr::include_graphics("man/figures/causactDemo.gif")
+knitr::include_graphics("man/figures/introScreenshot.png")
 ```
 
 This package is solely R-based, but behind the scenes, it relies on the `numpyro` Python package for Bayesian inference. 

README.md

@@ -28,7 +28,7 @@ Bayesian inference.
 > acyclic graphs with nested or overlapping plates. Improvements to
 > model construction for those more complex models is forthcoming.
 
-<img src="man/figures/causactDemo.gif" width="40%" style="display: block; margin: auto;" />
+<img src="man/figures/introScreenshot.png" width="40%" style="display: block; margin: auto;" />
 
 This package is solely R-based, but behind the scenes, it relies on the
 `numpyro` Python package for Bayesian inference.

npoExamples.R

@@ -1,7 +1,3 @@
-### Script contains five examples from
-### https://greta-stats.org/articles/example_models.html
-
-library(tidyverse)
 library(causact)
 
 graph = dag_create() %>%
@@ -21,6 +17,9 @@ graph = dag_create() %>%
 graph %>% dag_render()
 drawsDF = graph %>% dag_numpyro()
 drawsDF %>% dagp_plot()
+
+
+
 drawsDF %>% dagp_plot(abbrevLabels = TRUE)
 drawsDF %>% dagp_plot(densityPlot = TRUE)
 drawsDF %>% dagp_plot(densityPlot = TRUE, abbrevLabels = TRUE)

tests/testthat/test-dag_render2.R

@@ -0,0 +1,24 @@
+test_that("double plate graph can be rendered", {
+ renderGraph = dag_create() %>%
+ dag_node("# of Hot Day Beachgoers") %>%
+ dag_node(c("Rent by Location",
+ "Beachgoer Location Probability"),
+ obs = TRUE) %>%
+ dag_node(c("Revenue","Expenses","Profit"),
+ det = TRUE) %>%
+ dag_node("Bathhouse Location",
+ dec = TRUE) %>%
+ dag_edge(from = c("# of Hot Day Beachgoers",
+ "Beachgoer Location Probability",
+ "Bathhouse Location"),
+ to = "Revenue") %>%
+ dag_edge(from = c("Bathhouse Location",
+ "Rent by Location"),
+ to = "Expenses") %>%
+ dag_edge(from = c("Revenue","Expenses"),
+ to = "Profit") %>%
+ dag_plate("plate1","plate1",nodeLabels = "Revn") %>%
+ dag_plate("plate2","plate2",nodeLabels = "Expn") %>%
+ dag_render()
+ expect_type(renderGraph, "list")
+})