fix a few lints

Thanks `{flint}`

tests/testthat/test-bayesfactor_parameters.R

@@ -32,22 +32,22 @@ test_that("bayesfactor_parameters data frame", {
 
   # interval
   expect_warning(
-    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 0),
+    bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-0.1, 0.1), direction = 0),
     regexp = NA
   )
   expect_equal(bfsd$log_BF, c(0.13, 0.13), tolerance = 0.1)
 
-  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = 1)
+  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-0.1, 0.1), direction = 1)
   expect_equal(bfsd$log_BF, c(0.47, -0.39), tolerance = 0.1)
 
-  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, .1), direction = -1)
+  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-0.1, 0.1), direction = -1)
   expect_equal(bfsd$log_BF, c(-0.39, 0.47), tolerance = 0.1)
 
   # interval with inf
-  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-.1, Inf))
+  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-0.1, Inf))
   expect_equal(bfsd$log_BF, c(-0.81, 0.80), tolerance = 0.1)
 
-  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-Inf, .1))
+  bfsd <- bayesfactor_parameters(Xposterior, prior = Xprior, null = c(-Inf, 0.1))
   expect_equal(bfsd$log_BF, c(0.80, -0.81), tolerance = 0.1)
 })
 

tests/testthat/test-bayesfactor_restricted.R

@@ -8,9 +8,9 @@ test_that("bayesfactor_restricted df", {
   )
 
   posterior <- data.frame(
-    X = distribution_normal(100, .4, .2),
-    X1 = distribution_normal(100, -.2, .2),
-    X3 = distribution_normal(100, .2)
+    X = distribution_normal(100, 0.4, 0.2),
+    X1 = distribution_normal(100, -0.2, 0.2),
+    X3 = distribution_normal(100, 0.2)
   )
 
   hyps <- c(

tests/testthat/test-brms.R

@@ -10,7 +10,7 @@ test_that("brms", {
   expect_s3_class(hdi(model), "data.frame")
   expect_s3_class(ci(model), "data.frame")
   expect_s3_class(rope(model, verbose = FALSE), "data.frame")
-  expect_true("equivalence_test" %in% class(equivalence_test(model)))
+  expect_true(inherits(equivalence_test(model), "equivalence_test"))
   expect_s3_class(map_estimate(model), "data.frame")
   expect_s3_class(p_map(model), "data.frame")
   expect_s3_class(p_direction(model), "data.frame")

tests/testthat/test-effective_sample.R

@@ -11,7 +11,7 @@ test_that("effective_sample", {
     data.frame(
       Parameter = c("b_Intercept", "b_wt", "b_cyl"),
       ESS = c(5242, 2071, 1951),
-      stringsAsFactors = F
+      stringsAsFactors = FALSE
     )
   )
 
@@ -22,7 +22,7 @@ test_that("effective_sample", {
     data.frame(
       Parameter = c("b_Intercept"),
       ESS = c(2888),
-      stringsAsFactors = F
+      stringsAsFactors = FALSE
     )
   )
 
@@ -33,7 +33,7 @@ test_that("effective_sample", {
     data.frame(
       Parameter = c("b_Intercept"),
       ESS = c(1059),
-      stringsAsFactors = F
+      stringsAsFactors = FALSE
     )
   )
 })

tests/testthat/test-emmGrid.R

@@ -94,7 +94,7 @@ test_that("emmGrid p_significance", {
 })
 
 test_that("emmGrid rope", {
-  xrope <- rope(all_, range = "default", ci = .9)
+  xrope <- rope(all_, range = "default", ci = 0.9)
   expect_equal(length(xrope$ROPE_Percentage), 3)
 })
 

tests/testthat/test-rstanarm.R

@@ -38,7 +38,7 @@ test_that("rstanarm", {
   expect_s3_class(hdi(model), "data.frame")
   expect_s3_class(ci(model), "data.frame")
   expect_s3_class(rope(model, verbose = FALSE), "data.frame")
-  expect_true("equivalence_test" %in% class(equivalence_test(model)))
+  expect_true(inherits(equivalence_test(model), "equivalence_test"))
   expect_s3_class(map_estimate(model), "data.frame")
   expect_s3_class(p_map(model), "data.frame")
   expect_s3_class(p_direction(model), "data.frame")

tests/testthat/test-spi.R

@@ -6,11 +6,11 @@ test_that("spi", {
   skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("BayesFactor")
 
-  expect_equal(spi(distribution_normal(1000), ci = .90)$CI_low[1], -1.65, tolerance = 0.02)
-  expect_equal(nrow(spi(distribution_normal(1000), ci = c(.80, .90, .95))), 3, tolerance = 0.01)
+  expect_equal(spi(distribution_normal(1000), ci = 0.90)$CI_low[1], -1.65, tolerance = 0.02)
+  expect_equal(nrow(spi(distribution_normal(1000), ci = c(0.80, 0.90, 0.95))), 3, tolerance = 0.01)
   expect_equal(spi(distribution_normal(1000), ci = 1)$CI_low[1], -3.29, tolerance = 0.02)
   expect_equal(nchar(capture.output(print(spi(distribution_normal(1000))))), 22)
-  expect_equal(length(capture.output(print(spi(distribution_normal(1000), ci = c(.80, .90))))), 5)
+  expect_equal(length(capture.output(print(spi(distribution_normal(1000), ci = c(0.80, 0.90))))), 5)
 
 
   expect_error(spi(c(2, 3, NA)))
@@ -63,7 +63,7 @@ test_that("ci - BayesFactor", {
   skip_if_not_or_load_if_installed("httr")
   skip_if_not_or_load_if_installed("BayesFactor")
 
-  mod_bf <- proportionBF(y = 15, N = 25, p = .5)
+  mod_bf <- proportionBF(y = 15, N = 25, p = 0.5)
   p_bf <- insight::get_parameters(mod_bf)
 
   expect_equal(