Running ‘testthat.R’ [57s/81s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(mfGARCH)
  > 
  > test_check("mfGARCH")
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = date"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = date"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = week"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = week"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = week"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = week"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "No frequency specified for calculating the variance ratio - default: low.freq = week"
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "Specifying two covariates may lead to long estimation times."
  ── 1. Error: Esimtation two covariates (@test-estimation.R#73)  ────────────────
  there is no package called 'dplyr'
  1: expect_equal(fit_mfgarch(data = dplyr::filter(df_mfgarch, is.na(vix) == FALSE), y = "return", 
         x = "vix", low.freq = "date", K = 12, x.two = "nai", K.two = 36, low.freq.two = "year_month")$variance.ratio, 
         71.7362411162589) at testthat/test-estimation.R:73
  2: quasi_label(enquo(object), label)
  3: eval_bare(get_expr(quo), get_env(quo))
  4: fit_mfgarch(data = dplyr::filter(df_mfgarch, is.na(vix) == FALSE), y = "return", 
         x = "vix", low.freq = "date", K = 12, x.two = "nai", K.two = 36, low.freq.two = "year_month")
  5: "date" %in% colnames(data)
  6: colnames(data)
  7: is.data.frame(x)
  8: dplyr::filter
  9: getExportedValue(pkg, name)
  10: asNamespace(ns)
  11: getNamespace(ns)
  12: tryCatch(loadNamespace(name), error = function(e) stop(e))
  13: tryCatchList(expr, classes, parentenv, handlers)
  14: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  15: value[[3L]](cond)
  
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  [1] "For ensuring numerical stability of the parameter optimization and inversion of the Hessian, it is best to multiply log returns by 100."
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 11 SKIPPED: 0 FAILED: 1
  1. Error: Esimtation two covariates (@test-estimation.R#73) 
  
  Error: testthat unit tests failed
  Execution halted