Fixed errors and warnings triggered by unit tests in R 4.1 (current R…

…-devel):

* Explicitly ignored environment when testing equality of functions; this was the default behaviour until R 4.0 but will change in R 4.1 [1] and caused check errors on CRAN.
* Suppressed expected warnings due to unsufficient MCMC samples (done intentionally in tests to increase speed).
* Updated outdated URLs.

[1] https://stat.ethz.ch/pipermail/r-devel/2020-December/080172.html

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: CausalImpact
 Title: Inferring Causal Effects using Bayesian Structural Time-Series Models
-Date: 2019-12-26
+Date: 2020-12-21
 Author: Kay H. Brodersen <kbrodersen@google.com>,
   Alain Hauser <alhauser@google.com>
 Maintainer: Alain Hauser <alhauser@google.com>
@@ -9,8 +9,8 @@ Description: Implements a Bayesian approach to causal impact estimation in time
   series, as described in Brodersen et al. (2015) <DOI:10.1214/14-AOAS788>.
   See the package documentation on GitHub
   <https://google.github.io/CausalImpact/> to get started.
-Copyright: Copyright (C) 2014-2019 Google, Inc.
-Version: 1.2.4
+Copyright: Copyright (C) 2014-2020 Google, Inc.
+Version: 1.2.5
 VignetteBuilder: knitr
 License: Apache License 2.0 | file LICENSE
 Imports: assertthat (>= 0.2.0), Boom, dplyr, ggplot2, zoo

R/impact_analysis.R

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

R/impact_model.R

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.

README.md

@@ -35,12 +35,12 @@ library(CausalImpact)
 ## Further resources
 
 *   Manuscript:
-    [Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html)
+    [Brodersen et al., Annals of Applied Statistics (2015)](https://research.google/pubs/pub41854/)
 
 *   For questions on the statistics behind CausalImpact:
-    [Cross Validated](http://stats.stackexchange.com/questions/tagged/causalimpact)
+    [Cross Validated](https://stats.stackexchange.com/questions/tagged/causalimpact)
 
 *   For questions on how to use the CausalImpact R package:
-    [Stack Overflow](http://stackoverflow.com/questions/tagged/causalimpact)
+    [Stack Overflow](https://stackoverflow.com/questions/tagged/causalimpact)
 
 *   [Bug reports](https://github.com/google/CausalImpact/issues)

inst/CITATION

@@ -3,7 +3,7 @@ bibentry(
     textVersion =
         paste0("Brodersen et al., 2015, Annals of Applied Statistics. ",
                "Inferring causal impact using Bayesian structural time-series ",
-               "models. http://research.google.com/pubs/pub41854.html"),
+               "models. https://research.google/pubs/pub41854/"),
     header = "To cite CausalImpact in publications use:",
 
     title = paste0("Inferring causal impact using {B}ayesian structural ",
@@ -18,5 +18,5 @@ bibentry(
     year = "2014",
     volume = "9",
     pages = "247--274",
-    url = "http://research.google.com/pubs/pub41854.html"
+    url = "https://research.google/pubs/pub41854/"
 )

tests/testthat/test-impact-analysis.R

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -408,8 +408,9 @@ test_that("CausalImpact.RunWithData.MissingValues", {
   pre.period <- c(1, 20)
   post.period <- c(21, 30)
   model.args <- list(niter = 100)
-  expect_error(impact <- CausalImpact(data, pre.period, post.period,
-                                      model.args),
+  expect_error(suppressWarnings(impact <- CausalImpact(data, pre.period,
+                                                       post.period,
+                                                       model.args)),
                NA)
   # Test that all columns in the result series except those associated with
   # point predictions have missing values at the time points the result time
@@ -469,8 +470,9 @@ test_that("CausalImpact.RunWithData.StandardizeData", {
               tolerance = 0.001)
   expect_equal(impact1$series, impact2$series, tolerance = 0.1)
 
-  # Recover ground truth (generative model with 1 covariate)
-  # with/without <standardize.data>
+  # Recover ground truth (generative model with 1 covariate) with/without
+  # <standardize.data>. Suppressing the warning that there might not be enough
+  # MCMC samples in CausalImpact calls.
   set.seed(1)
   n <- 500
   x1 <- sin(1:n / 100) + rnorm(n, 0, 0.1)
@@ -480,14 +482,14 @@ test_that("CausalImpact.RunWithData.StandardizeData", {
   pre.period <- c(1, 250)
   post.period <- c(251, 500)
   data <- cbind(y, x1)
-  impact1 <- CausalImpact(data, pre.period, post.period,
-                          model.args = list(niter = 500,
-                                            standardize.data = FALSE))
+  suppressWarnings(impact1 <- CausalImpact(
+    data, pre.period, post.period, 
+    model.args = list(niter = 500, standardize.data = FALSE)))
   estimates1 <- colMeans(impact1$model$bsts.model$coefficients)
   expect_equal(as.vector(estimates1)[2], beta[2], tolerance = 0.05)
-  impact2 <- CausalImpact(data, pre.period, post.period,
-                          model.args = list(niter = 500,
-                                            standardize.data = TRUE))
+  suppressWarnings(impact2 <- CausalImpact(
+    data, pre.period, post.period,
+    model.args = list(niter = 500, standardize.data = TRUE)))
   estimates2 <- colMeans(impact2$model$bsts.model$coefficients)
   expect_equal(as.vector(estimates2)[2], 1, tolerance = 0.05)
 })
@@ -577,13 +579,16 @@ test_that("CausalImpact.RunWithData.MissingTimePoint", {
   post.period <- as.Date(c("2017-01-16", "2017-01-20"))
   model.args <- list(niter = 100)
 
-  # Missing in pre-period
-  impact <- CausalImpact(series[-10, ], pre.period, post.period, model.args)
+  # Missing in pre-period. Suppressing the warning that there might not be
+  # enough MCMC samples.
+  suppressWarnings(impact <- CausalImpact(series[-10, ], pre.period, 
+                                          post.period, model.args))
   indices <- time(impact$series)
   expect_equal(indices, time(series)[-10])
 
   # Missing in post-period
-  impact <- CausalImpact(series[-17, ], pre.period, post.period, model.args)
+  suppressWarnings(impact <- CausalImpact(series[-17, ], pre.period,
+                                          post.period, model.args))
   indices <- time(impact$series)
   expect_equal(indices, time(series)[-17])
 })

tests/testthat/test-impact-inference.R

@@ -1,4 +1,4 @@
-# Copyright 2014 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -28,7 +28,7 @@ test_that("GetPosteriorStateSamples", {
   model.args <- list(niter = 100)
 
   # Create a healthy bsts.model and test it
-  bsts.model <- ConstructModel(data, model.args)
+  suppressWarnings(bsts.model <- ConstructModel(data, model.args))
   state.samples <- GetPosteriorStateSamples(bsts.model)
   expect_equal(ncol(state.samples), 365)
 })
@@ -43,7 +43,7 @@ test_that("ComputeResponseTrajectories", {
   # Test healthy input
   data <- zoo(cbind(rnorm(365), rnorm(365), rnorm(365)))
   model.args <- list(niter = 100)
-  bsts.model <- ConstructModel(data, model.args)
+  suppressWarnings(bsts.model <- ConstructModel(data, model.args))
   y.samples <- ComputeResponseTrajectories(bsts.model)
   expect_equal(ncol(y.samples), 365)
 })

tests/testthat/test-impact-misc.R

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -187,7 +187,8 @@ test_that("StandardizeAllVariables", {
     expect_equal(mean(result$data[, column]), 0, tolerance = 0.0001);
     expect_equal(sd(result$data[, column]), 1, tolerance = 0.0001)
   })
-  expect_equal(result$UnStandardize, Standardize(data[, 1])$UnStandardize)
+  expect_equal(result$UnStandardize, Standardize(data[, 1])$UnStandardize,
+               check.environment = FALSE)
 
   # Test that several columns are standardized correctly when fitting mean and
   # SD only over part of the rows.
@@ -196,7 +197,8 @@ test_that("StandardizeAllVariables", {
     expect_equal(mean(result$data[11 : 90, column]), 0, tolerance = 0.0001);
     expect_equal(sd(result$data[11 : 90, column]), 1, tolerance = 0.0001)
   })
-  expect_equal(result$UnStandardize, Standardize(data[, 1])$UnStandardize)
+  expect_equal(result$UnStandardize, Standardize(data[, 1])$UnStandardize,
+               check.environment = FALSE)
 
   # Test healthy input: single series only
   set.seed(1)
@@ -206,14 +208,16 @@ test_that("StandardizeAllVariables", {
   expect_equal(names(result), c("data", "UnStandardize"))
   expect_equal(mean(result$data), 0, tolerance = 0.0001)
   expect_equal(sd(result$data), 1, tolerance = 0.0001)
-  expect_equal(result$UnStandardize, Standardize(data)$UnStandardize)
+  expect_equal(result$UnStandardize, Standardize(data)$UnStandardize,
+               check.environment = FALSE)
 
   # Test that a single series is standardized correctly when fitting mean and SD
   # only over part of the data range.
   result <- StandardizeAllVariables(data, c(11, 90))
   expect_equal(mean(result$data[11: 90]), 0, tolerance = 0.0001)
   expect_equal(sd(result$data[11 : 90]), 1, tolerance = 0.0001)
-  expect_equal(result$UnStandardize, Standardize(data)$UnStandardize)
+  expect_equal(result$UnStandardize, Standardize(data)$UnStandardize,
+               check.environment = FALSE)
 })
 
 test_that("GetPeriodIndices.InvalidInput", {

tests/testthat/test-impact-model.R

@@ -1,4 +1,4 @@
-# Copyright 2014-2017 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -28,16 +28,29 @@ test_that("ObservationsAreIllConditioned", {
   expect_false(ObservationsAreIllConditioned(c(NA, NA, 1, NA, 2, 3, NA, NA)))
 
   # Test all NA
-  expect_true(ObservationsAreIllConditioned(c(NA, NA, NA, NA, NA)))
+  expect_warning(ObservationsAreIllConditioned(c(NA, NA, NA, NA, NA)), "all NA")
+  expect_true(suppressWarnings(
+    ObservationsAreIllConditioned(c(NA, NA, NA, NA, NA))))
 
   # Test fewer than 3 non-NA values
   expect_error(ObservationsAreIllConditioned(NULL))
   expect_error(ObservationsAreIllConditioned(c()))
-  expect_true(ObservationsAreIllConditioned(c(1)))
-  expect_true(ObservationsAreIllConditioned(c(1, 2)))
-  expect_true(ObservationsAreIllConditioned(c(1, 2, NA)))
-  expect_true(ObservationsAreIllConditioned(c(NA, 1, 2, NA)))
-  expect_true(ObservationsAreIllConditioned(c(NA, 1, 2, NA, NA)))
+  expect_warning(ObservationsAreIllConditioned(c(1)),
+                 "fewer than 3 non-NA values")
+  expect_true(suppressWarnings(ObservationsAreIllConditioned(c(1))))
+  expect_warning(ObservationsAreIllConditioned(c(1, 2)),
+                 "fewer than 3 non-NA values")
+  expect_true(suppressWarnings(ObservationsAreIllConditioned(c(1, 2))))
+  expect_warning(ObservationsAreIllConditioned(c(1, 2, NA)),
+                 "fewer than 3 non-NA values")
+  expect_true(suppressWarnings(ObservationsAreIllConditioned(c(1, 2, NA))))
+  expect_warning(ObservationsAreIllConditioned(c(NA, 1, 2, NA)),
+                 "fewer than 3 non-NA values")
+  expect_true(suppressWarnings(ObservationsAreIllConditioned(c(NA, 1, 2, NA))))
+  expect_warning(ObservationsAreIllConditioned(c(NA, 1, 2, NA, NA)),
+                 "fewer than 3 non-NA values")
+  expect_true(suppressWarnings(
+    ObservationsAreIllConditioned(c(NA, 1, 2, NA, NA))))
 })
 
 test_that("FormatInputForConstructModel", {

tests/testthat/test-impact-plot.R

@@ -1,4 +1,4 @@
-# Copyright 2014 Google Inc. All rights reserved.
+# Copyright 2014-2020 Google Inc. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -121,13 +121,13 @@ test_that("CreateImpactPlot", {
   # Test plot.CausalImpact() generic
   q1 <- CreateImpactPlot(impact)
   q2 <- plot(impact)  # dispatched to plot.CausalImpact()
-  expect_equal(q1, q2)
+  expect_equal(q1, q2, check.environment = FALSE)
 
   # Test plotting different metrics
   q1 <- plot(impact)
   q2 <- plot(impact, c("original", "pointwise", "cumulative"))
   q3 <- plot(impact, c("o", "point", "c"))
-  expect_equal(q1, q2)
+  expect_equal(q1, q2, check.environment = FALSE)
   # As of ggplot 2.0.0, `q1` and `q2` are still the same but `q3` is different.
   # This is because `q1` and `q2` contains:
   # > q1$plot_env$metrics
@@ -139,10 +139,10 @@ test_that("CreateImpactPlot", {
   #  "original"  "pointwise" "cumulative"
   # So we test whether `q1` equals `q3` except for `$plot_env$metrics`.
   q3$plot_env$metrics <- q2$plot_env$metrics
-  expect_equal(q1, q3)
+  expect_equal(q1, q3, check.environment = FALSE)
 
   # Test different order
   q1 <- plot(impact, c("p", "c"))
   q2 <- plot(impact, c("c", "p"))
-  expect_true(!isTRUE(all.equal(q1, q2)))
+  expect_true(!isTRUE(all.equal(q1, q2, check.environment = FALSE)))
 })

vignettes/CausalImpact.Rmd

@@ -35,7 +35,7 @@ evolved after the intervention if the intervention had never occurred. For a
 quick overview, watch the
 [tutorial video](https://www.youtube.com/watch?v=GTgZfCltMm8).
 For details, see:
-[Brodersen et al., Annals of Applied Statistics (2015)](http://research.google.com/pubs/pub41854.html).
+[Brodersen et al., Annals of Applied Statistics (2015)](https://research.google/pubs/pub41854/).
 
 #### What assumptions does the model make?
 
@@ -332,7 +332,7 @@ We recommend referencing the use of the CausalImpact R package as shown in the
 example below:
 
 "CausalImpact `r packageVersion("CausalImpact")`, Brodersen et al., Annals of
-Applied Statistics (2015). http://google.github.io/CausalImpact/"
+Applied Statistics (2015). https://google.github.io/CausalImpact/"
 
 To find out which package version you are using, type
 `packageVersion("CausalImpact")`. See the bottom of this page for full
@@ -422,11 +422,11 @@ the plot, see
 Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL.
 Inferring causal impact using Bayesian structural time-series models.
 *Annals of Applied Statistics*, 2015, Vol. 9, No. 1, 247-274.
-http://research.google.com/pubs/pub41854.html
+https://research.google/pubs/pub41854/
 
 ### Discussion forum
 
-http://stats.stackexchange.com/
+https://stats.stackexchange.com/
 
 
 - - -