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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(BKP)
> 
> test_check("BKP")

       Beta Kernel Process (BKP) Model    

Number of observations (n):  30
Input dimensionality (d):    1
Kernel type:                 gaussian
Optimized kernel parameters: 0.0204
Minimum achieved loss:       0.01100
Loss function:               brier
Prior type:                  noninformative

       Beta Kernel Process (BKP) Model   

Number of observations (n):  30
Input dimensionality (d):    1
Kernel type:                 gaussian
Optimized kernel parameters: 0.0204
Minimum achieved loss:       0.01100
Loss function:               brier
Prior type:                  noninformative

Posterior predictive summary (training points):
                      Mean Median     SD    Min    Max
Posterior means     0.4756 0.5034 0.1999 0.1329 0.8784
Posterior variances 0.0032 0.0028 0.0029 0.0007 0.0153
Prediction results on training data (X).
Total number of training points: 30 

Preview of predictions for training data (first 6 of 30 points):
       x   mean variance 2.5% quantile 97.5% quantile
 -0.8497 0.3246   0.0029        0.2245         0.4335
  1.1532 0.4941   0.0029        0.3889         0.5996
 -0.3641 0.4503   0.0063        0.2983         0.6072
  1.5321 0.5535   0.0012        0.4859         0.6202
  1.7619 0.5039   0.0023        0.4093         0.5983
 -1.8178 0.5184   0.0047        0.3836         0.6520
 ...
Simulation results on training data (X).
Total number of training points: 30 
Number of posterior draws (nsim): 1 

Preview of simulations for training data (first 6 of 30 points):

--- Posterior Probability Simulations ---
       x   sim1
 -0.8497 0.3417
  1.1532 0.4619
 -0.3641 0.3517
  1.5321 0.5629
  1.7619 0.5298
 -1.8178 0.5800
 ...

       Dirichlet Kernel Process (DKP) Model   

Number of observations (n):  30
Input dimensionality (d):    1
Number of classes (q):       3
Kernel type:                 gaussian
Optimized kernel parameters: 0.0673
Minimum achieved loss:       0.00490
Loss function:               brier
Prior type:                  noninformative

      Summary of Dirichlet Kernel Process (DKP) Model   

Number of observations (n):  30
Input dimensionality (d):    1
Number of classes (q):       3
Kernel type:                 gaussian
Optimized kernel parameters: 0.0673
Minimum achieved loss:       0.00490
Loss function:               brier
Prior type:                  noninformative

Posterior predictive summary (training points):

Class 1:
                      Mean Median     SD    Min    Max
Posterior means     0.3042 0.3883 0.1963 0.0054 0.5092
Posterior variances 0.0006 0.0005 0.0004 0.0000 0.0017

Class 2:
                      Mean Median     SD    Min    Max
Posterior means     0.2354 0.2419 0.0839 0.1076 0.3914
Posterior variances 0.0006 0.0005 0.0003 0.0002 0.0015

Class 3:
                      Mean Median     SD    Min    Max
Posterior means     0.4604 0.4326 0.2142 0.2229 0.7998
Posterior variances 0.0007 0.0007 0.0003 0.0003 0.0015
Prediction results on training data (X).
Total number of training points: 30 

Preview of predictions for training data (first 6 of 30 points):

Class 1 predictions:
       x   Mean Variance 2.5% Quantile 97.5% Quantile
 -0.8497 0.0491   0.0001        0.0282         0.0753
  1.1532 0.4780   0.0017        0.3968         0.5597
 -0.3641 0.1470   0.0005        0.1073         0.1917
  1.5321 0.5051   0.0005        0.4604         0.5497
  1.7619 0.5092   0.0005        0.4643         0.5540
 -1.8178 0.0054   0.0000        0.0001         0.0198
 ...

Class 2 predictions:
       x   Mean Variance 2.5% Quantile 97.5% Quantile
 -0.8497 0.1511   0.0004        0.1140         0.1924
  1.1532 0.2397   0.0013        0.1736         0.3127
 -0.3641 0.3380   0.0008        0.2827         0.3956
  1.5321 0.2700   0.0004        0.2313         0.3106
  1.7619 0.2680   0.0004        0.2292         0.3086
 -1.8178 0.2300   0.0009        0.1725         0.2930
 ...

Class 3 predictions:
       x   Mean Variance 2.5% Quantile 97.5% Quantile
 -0.8497 0.7998   0.0005        0.7542         0.8418
  1.1532 0.2824   0.0014        0.2119         0.3586
 -0.3641 0.5150   0.0009        0.4552         0.5745
  1.5321 0.2249   0.0004        0.1887         0.2632
  1.7619 0.2229   0.0004        0.1867         0.2613
 -1.8178 0.7646   0.0010        0.7012         0.8227
 ...
Simulation results on training data (X).
Total number of training points: 30 
Number of posterior draws (nsim): 1 

Preview of simulations for training data (first 6 of 30 points):

--- Posterior Probability Simulations ---

Simulation 1 :
       x Class1 Class2 Class3
 -0.8497 0.0507 0.1495 0.7997
  1.1532 0.4730 0.2604 0.2666
 -0.3641 0.1638 0.2965 0.5397
  1.5321 0.4803 0.2644 0.2553
  1.7619 0.5019 0.2912 0.2069
 -1.8178 0.0024 0.2336 0.7640
 ...
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 310 ]
> 
> proc.time()
   user  system elapsed 
  31.56    2.46   34.01