bayes_mode.md
In BayesMultiMode: Bayesian Mode Inference

bayes_mode works with external MCMC output

Code
  summary(bayesmix)
Output
  Mixture estimated with a Bayesian MCMC method.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: NA
  - Number of distribution variables: 4
  - Names of variables: mu sigma xi nu

  Summary of MCMC output after burnin:
  this table can be reproduced with: summarise_draws(bayesmix$mcmc)

  Note that label-switching might occur in the MCMC draws becayse BayesMultiMode does not carry out post-processing. 
  While label-switching does not affect mode inference it can affect diagnostic checks.
Message

Code
  summary(bayesmode)
Output
  Posterior probability of multimodality is 1

  Inference results on the number of modes:
    p_nb_modes (matrix, dim 1x2): 
       number of modes posterior probability
  [1,]               2                     1

  Inference results on mode locations:
    p_loc (matrix, dim 55x2): 
       mode location posterior probability
  [1,]           0.5                     1
  [2,]           0.6                     0
  [3,]           0.7                     0
  [4,]           0.8                     0
  [5,]           0.9                     0
  [6,]           1.0                     0
  ... (49 more rows)

bayes_mode works with normal mixture

Code
  summary(bayesmix)
Output
  Mixture estimated with a Bayesian MCMC method.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: normal
  - Number of distribution variables: 2
  - Names of variables: mu sigma

  Summary of MCMC output after burnin:
  this table can be reproduced with: summarise_draws(bayesmix$mcmc)

  Note that label-switching might occur in the MCMC draws becayse BayesMultiMode does not carry out post-processing. 
  While label-switching does not affect mode inference it can affect diagnostic checks.
Message

Code
  summary(bayesmode)
Output
  Posterior probability of multimodality is 1

  Inference results on the number of modes:
    p_nb_modes (matrix, dim 1x2): 
       number of modes posterior probability
  [1,]               2                     1

  Inference results on mode locations:
    p_loc (matrix, dim 107x2): 
       mode location posterior probability
  [1,]          -5.2                 0.055
  [2,]          -5.1                 0.000
  [3,]          -5.0                 0.415
  [4,]          -4.9                 0.225
  [5,]          -4.8                 0.045
  [6,]          -4.7                 0.000
  ... (101 more rows)

bayes_mode works with skew_normal mixture

Code
  summary(bayesmix)
Output
  Mixture estimated with a Bayesian MCMC method.
  - Mixture type: continuous
  - Number of components: 2
  - Distribution family: skew_normal
  - Number of distribution variables: 3
  - Names of variables: xi omega alpha

  Summary of MCMC output after burnin:
  this table can be reproduced with: summarise_draws(bayesmix$mcmc)

  Note that label-switching might occur in the MCMC draws becayse BayesMultiMode does not carry out post-processing. 
  While label-switching does not affect mode inference it can affect diagnostic checks.
Message

Code
  summary(bayesmode)
Output
  Posterior probability of multimodality is 1

  Inference results on the number of modes:
    p_nb_modes (matrix, dim 1x2): 
       number of modes posterior probability
  [1,]               2                     1

  Inference results on mode locations:
    p_loc (matrix, dim 104x2): 
       mode location posterior probability
  [1,]          -5.2                0.0050
  [2,]          -5.1                0.0000
  [3,]          -5.0                0.2800
  [4,]          -4.9                0.3600
  [5,]          -4.8                0.2075
  [6,]          -4.7                0.0675
  ... (98 more rows)

bayes_mode works with shifted poisson mixture

Code
  summary(bayesmix)
Output
  Mixture estimated with a Bayesian MCMC method.
  - Mixture type: discrete
  - Number of components: 2
  - Distribution family: shifted_poisson
  - Number of distribution variables: 2
  - Names of variables: kappa lambda

  Summary of MCMC output after burnin:
  this table can be reproduced with: summarise_draws(bayesmix$mcmc)

  Note that label-switching might occur in the MCMC draws becayse BayesMultiMode does not carry out post-processing. 
  While label-switching does not affect mode inference it can affect diagnostic checks.
Message

Code
  summary(bayesmode)
Output
  Posterior probability of multimodality is 0.9975

  Inference results on the number of modes:
    p_nb_modes (matrix, dim 2x2): 
       number of modes posterior probability
  [1,]               1                0.0025
  [2,]               2                0.9975

  Inference results on mode locations:
    p_loc (matrix, dim 7x2): 
       mode location posterior probability
  [1,]             0                0.8525
  [2,]             1                0.1475
  [3,]             2                0.0000
  [4,]             3                0.0000
  [5,]             4                0.1325
  [6,]             5                0.8275
  ... (1 more rows)

bayes_mode works with poisson mixture

Code
  summary(bayesmix)
Output
  Mixture estimated with a Bayesian MCMC method.
  - Mixture type: discrete
  - Number of components: 2
  - Distribution family: poisson
  - Number of distribution variables: 1
  - Names of variables: lambda

  Summary of MCMC output after burnin:
  this table can be reproduced with: summarise_draws(bayesmix$mcmc)

  Note that label-switching might occur in the MCMC draws becayse BayesMultiMode does not carry out post-processing. 
  While label-switching does not affect mode inference it can affect diagnostic checks.
Message

Code
  summary(bayesmode)
Output
  Posterior probability of multimodality is 1

  Inference results on the number of modes:
    p_nb_modes (matrix, dim 1x2): 
       number of modes posterior probability
  [1,]               2                     1

  Inference results on mode locations:
    p_loc (matrix, dim 12x2): 
       mode location posterior probability
  [1,]             0                     1
  [2,]             1                     0
  [3,]             2                     0
  [4,]             3                     0
  [5,]             4                     0
  [6,]             5                     0
  ... (6 more rows)