Nothing

```
context("man file example tests")
# From envcpt.Rd & plot.fit.envcpt.Rd (same examples)
if(identical(Sys.getenv("NOT_CRAN"), "true")) {
set.seed(1)
x=c(rnorm(100,0,1),rnorm(100,5,1))
out=envcpt(x) # run all models with default values
test_that('envcpt1',expect_equal_to_reference(out[[1]],file='envcpt1.rds'))
test_that("envcpt2",expect_equal_to_reference(AIC(out),file="envcpt2.rds"))
test_that("envcpt3",expect_equivalent(which.min(AIC(out)),2))
test_that("envcpt4",expect_is(out$meancpt,"cpt"))
test_that("envcpt5",expect_equivalent(cpts(out$meancpt),100))
test_that("envcpt5AICweights",expect_equal_to_reference(AICweights(out),file='envcpt5AICweights.rds'))
test_that("envcpt5BIC",expect_equal_to_reference(BIC(out),file='envcpt5BIC.rds'))
test_that("envcpt5BICmin",expect_equivalent(which.min(BIC(out)),2))
test_that("envcpt5plot",expect_silent(plot(out,type='fit')))
test_that("envcpt5aicplot",expect_silent(plot(out,type="aic")))
test_that("envcpt5aicplot",expect_silent(plot(out,type="bic")))
test_that("messages1",expect_message(envcpt(x),"Fitting 12 models"))
}
if (identical(Sys.getenv("NOT_CRAN"), "true")) {
set.seed(10)
x=c(0.01*(1:100),1.5-0.02*((101:250)-101))+rnorm(250,0,0.2)
out=envcpt(x,minseglen=10) # run all models with a minimum of 10 observations between changes
test_that("envcpt6",expect_equal_to_reference(AIC(out),file='envcpt6.rds'))
test_that("envcpt7",expect_equivalent(which.min(AIC(out)),8))
test_that("envcpt8",expect_is(out$trendcpt,"cpt.reg"))
test_that("envcpt9",expect_equivalent(cpts(out$trendcpt),100))
test_that("envcpt9AICweights",expect_equal_to_reference(AICweights(out),file='envcpt9AICweights.rds'))
test_that("envcpt9BIC",expect_equal_to_reference(BIC(out),file='envcpt9BIC.rds'))
test_that("envcpt9BICmin",expect_equivalent(which.min(BIC(out)),8))
test_that("envcpt9plot",expect_silent(plot(out,type='fit')))
test_that("envcpt9aicplot",expect_silent(plot(out,type="aic")))
test_that("envcpt9bicplot",expect_silent(plot(out,type="bic")))
test_that("messages2",expect_message(envcpt(x),"Fitting 12 models"))
}
if (identical(Sys.getenv("NOT_CRAN"), "true")) {
set.seed(100)
x=arima.sim(model=list(ar=c(0.7,0.2)),n=500)+0.01*(1:500)
out=envcpt(x,models=c(3:6,9:12)) # runs a subset of models (those with AR components)
test_that("envcpt10",expect_equal_to_reference(AIC(out),file='envcpt10.rds'))
test_that("envcpt11",expect_equivalent(which.min(AIC(out)),10))
test_that("envcpt12",expect_is(out$trendar2,"lm"))
test_that("envcpt13",expect_equivalent(out$trendar2$coefficients,c(-0.069418907, 0.001702804, 0.661336574, 0.186613360)))
test_that("envcpt13AICweights",expect_equal_to_reference(AICweights(out),file='envcpt13AICweights.rds'))
test_that("envcpt13BIC",expect_equal_to_reference(BIC(out),file='envcpt13BIC.rds'))
test_that("envcpt13BICmin",expect_equivalent(which.min(BIC(out)),10))
test_that("envcpt13plot",expect_silent(plot(out,type='fit')))
test_that("envcpt13aicplot",expect_silent(plot(out,type="aic")))
test_that("envcpt13bicplot",expect_silent(plot(out,type="bic")))
test_that("messages3",expect_message(envcpt(x,models=c(3:6,9:12)),"Fitting 8 models"))
}
```

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