# print works as expected

    Code
      print(bt_covlmc)
    Output
      VLMC with covariate context tree on A, B, C 
       cutoff in quantile scale: 7.738e-07
       Number of contexts: 3 
       Maximum context length: 1 
       Selected by BIC (1044.993) with likelihood function "truncated" (-466.5832)

---

    Code
      print(at_covlmc)
    Output
      VLMC with covariate context tree on A, B, C 
       cutoff in quantile scale: 0.5
       Number of contexts: 3 
       Maximum context length: 1 
       Selected by AIC (947.2463) with likelihood function "truncated" (-449.6231)

# summary works as expected

    Code
      print(summary(bt_covlmc))
    Output
      VLMC with covariate tune results
      
      Best VLMC with covariate selected by BIC (1044.993) with likelihood function "truncated" (-466.5832)
      VLMC with covariate context tree on A, B, C 
       cutoff in quantile scale: 7.738e-07
       Number of contexts: 3 
       Maximum context length: 1 
      
      Pruning results
              alpha depth nb_contexts loglikelihood cov_depth       AIC      BIC
       5.000000e-01     1           3     -449.6231         1  947.2463 1048.349
       7.738076e-07     1           3     -466.5832         1  969.1665 1044.993
       1.454609e-13     1           3     -499.9600         1 1019.9199 1062.046
       1.950257e-17     0           1     -546.7455         0 1097.4909 1105.916

---

    Code
      print(summary(at_covlmc))
    Output
      VLMC with covariate tune results
      
      Best VLMC with covariate selected by AIC (947.2463) with likelihood function "truncated" (-449.6231)
      VLMC with covariate context tree on A, B, C 
       cutoff in quantile scale: 0.5
       Number of contexts: 3 
       Maximum context length: 1 
      
      Pruning results
              alpha depth nb_contexts loglikelihood cov_depth       AIC      BIC
       5.000000e-01     1           3     -449.6231         1  947.2463 1048.349
       7.738076e-07     1           3     -466.5832         1  969.1665 1044.993
       1.454609e-13     1           3     -499.9600         1 1019.9199 1062.046
       1.950257e-17     0           1     -546.7455         0 1097.4909 1105.916

# tune_vlmc verbosity is adequate

    Fitting a covlmc with max_depth= 100 and alpha= 0.5 
    Initial criterion= Inf 
    Improving criterion= 1072.785 
    Pruning covlmc with alpha= 1.323035e-09 
    VLMC with covariate context tree on A, B, C 
     cutoff in quantile scale: 0.5
     Number of contexts: 3 
     Maximum context length: 1 
     Selected by BIC (1072.785) with likelihood function "truncated" (-461.8413)

