skip tests on cran

tests/testthat/test-abstract.R

@@ -1,6 +1,8 @@
 context("ma_abstract")
 
 test_that("ma_abstract works as expected", {
+  skip_on_cran()
+
   vcr::use_cassette("ma_abstract", {
     aa <- ma_abstract(query = "Y='19'...", count = 10)
 

tests/testthat/test-calchist.R

@@ -1,6 +1,7 @@
 context("ma_calchist")
 
 # test_that("ma_calchist works as expected", {
+#   skip_on_cran()
 #   ## FIXME: add vcr back later, rate limit exceeded on this, must have changed to be very very few per time period
 #   vcr::use_cassette("ma_calchist", {
 #     aa <- ma_calchist(query = "And(Composite(AA.AuN=='jaime teevan'),Y>2012)",

tests/testthat/test-evaluate.R

@@ -1,6 +1,8 @@
 context("ma_evaluate")
 
 test_that("ma_evaluate works as expected", {
+  skip_on_cran()
+
   vcr::use_cassette("ma_evaluate", {
     aa <- ma_evaluate(query = "Y='19'...")
 

tests/testthat/test-interpret.R

@@ -1,6 +1,8 @@
 context("ma_interpret")
 
 test_that("ma_interpret works as expected", {
+  skip_on_cran()
+
   vcr::use_cassette("ma_interpret", {
     aa <- ma_interpret(query = "papers by jaime'...")
 

tests/testthat/test-search.R

@@ -1,6 +1,8 @@
 context("ma_search")
 
 test_that("ma_search works as expected", {
+  skip_on_cran()
+
   vcr::use_cassette("ma_search", {
     aa <- ma_search(query = "Y='19'...")
 

tests/testthat/test-similarity.R

@@ -1,6 +1,8 @@
 context("ma_similarity")
 
 test_that("ma_similarity works as expected", {
+  skip_on_cran()
+
   s1 <- "Using complementary priors, we derive a fast greedy algorithm that
 	can learn deep directed belief networks one layer at a time, provided the
 	top two layers form an undirected associative memory"