Running ‘testthat.R’ [90s/128s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(mixpoissonreg)
  > 
  > test_check("mixpoissonreg")
  
  Negative Binomial Regression - Expectation-Maximization Algorithm
  
  Call:
  mixpoissonreg(formula = daysabs ~ prog + math, data = Attendance, 
      em_controls = list(maxit = 1))
  
  Coefficients modeling the mean (with log link):
     (Intercept)   progAcademic progVocational           math 
     2.615321329   -0.440755734   -1.278742703   -0.005993368 
  Coefficients modeling the precision (with identity link):
  (Intercept) 
     1.032717 
  
  Negative Binomial Regression - Maximum-Likelihood Estimation
  
  Call:
  mixpoissonregML(formula = daysabs ~ prog + math, data = Attendance)
  
  Coefficients modeling the mean (with log link):
     (Intercept)   progAcademic progVocational           math 
     2.615321329   -0.440755734   -1.278742703   -0.005993368 
  Coefficients modeling the precision (with identity link):
  (Intercept) 
     1.032717 
  
  Negative Binomial Regression - Maximum-Likelihood Estimation
  
  Call:
  mixpoissonregML(formula = daysabs ~ prog + math, data = Attendance)
  
  
  Pearson residuals:
       RSS      Min       1Q   Median       3Q      Max 
  339.8789  -0.9611  -0.7098  -0.3259   0.2460   4.9565 
  
  Coefficients modeling the mean (with  link):
                  Estimate Std.error z-value Pr(>|z|)    
  (Intercept)     2.615321  0.196355  13.319  < 2e-16 ***
  progAcademic   -0.440756  0.182579  -2.414   0.0158 *  
  progVocational -1.278743  0.201982  -6.331 2.44e-10 ***
  math           -0.005993  0.002507  -2.390   0.0168 *  
  
  Coefficients modeling the precision (with  link):
              Estimate Std.error z-value Pr(>|z|)    
  (Intercept)   1.0327    0.1062   9.729   <2e-16 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
  
  Efron's pseudo R-squared:  0.1707781 
  Number of function calls by 'optim' = 4
  
  Negative Binomial Regression - Maximum-Likelihood Estimation
  
  Call:
  mixpoissonregML(formula = daysabs ~ prog + math, data = Attendance, 
      envelope = 10, optim_controls = list(maxit = 1))
  
  
  Pearson residuals:
       RSS      Min       1Q   Median       3Q      Max 
  339.8789  -0.9611  -0.7098  -0.3259   0.2460   4.9565 
  
  Coefficients modeling the mean (with  link):
                  Estimate Std.error z-value Pr(>|z|)    
  (Intercept)     2.615321  0.196355  13.319  < 2e-16 ***
  progAcademic   -0.440756  0.182579  -2.414   0.0158 *  
  progVocational -1.278743  0.201982  -6.331 2.44e-10 ***
  math           -0.005993  0.002507  -2.390   0.0168 *  
  
  Coefficients modeling the precision (with  link):
              Estimate Std.error z-value Pr(>|z|)    
  (Intercept)   1.0327    0.1062   9.729   <2e-16 ***
  ---
  Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 
  
  Efron's pseudo R-squared:  0.1707781 
  Percentage of residuals within the envelope = 79.2994
  Number of function calls by 'optim' = 4
  [ FAIL 1 | WARN 25 | SKIP 0 | PASS 41 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-tidy.R:14:1'): (code run outside of `test_that()`) ─────────────
  Error in `ll(object)`: could not find function "ll"
  Backtrace:
      ▆
   1. ├─generics::glance(fit1) at test-tidy.R:14:1
   2. ├─mixpoissonreg:::glance.mixpoissonreg(fit1)
   3. │ └─tibble::tibble(...)
   4. │   └─tibble:::tibble_quos(xs, .rows, .name_repair)
   5. │     └─rlang::eval_tidy(xs[[j]], mask)
   6. ├─stats::AIC(x)
   7. └─stats:::AIC.default(x)
  
  [ FAIL 1 | WARN 25 | SKIP 0 | PASS 41 ]
  Error: Test failures
  Execution halted