CRAN Package Check Results for Package fanovaGraph

Last updated on 2019-04-22 07:46:41 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.4.8 5.09 76.68 81.77 ERROR
r-devel-linux-x86_64-debian-gcc 1.4.8 4.34 57.65 61.99 ERROR
r-devel-linux-x86_64-fedora-clang 1.4.8 96.44 ERROR
r-devel-linux-x86_64-fedora-gcc 1.4.8 94.48 ERROR
r-devel-windows-ix86+x86_64 1.4.8 15.00 88.00 103.00 ERROR
r-patched-linux-x86_64 1.4.8 5.41 75.98 81.39 ERROR
r-patched-solaris-x86 1.4.8 149.70 ERROR
r-release-linux-x86_64 1.4.8 3.19 67.92 71.11 OK
r-release-windows-ix86+x86_64 1.4.8 5.00 106.00 111.00 OK
r-release-osx-x86_64 1.4.8 OK
r-oldrel-windows-ix86+x86_64 1.4.8 9.00 114.00 123.00 OK
r-oldrel-osx-x86_64 1.4.8 OK

Check Details

Version: 1.4.8
Check: tests
Result: ERROR
     Running 'run-all.R' [20s/21s]
    Running the tests in 'tests/run-all.R' failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     -- 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     -- 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     -- 3. Failure: thresholdIdentification works (@tests.R#93) --------------------
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     == testthat results ===========================================================
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.4.8
Check: tests
Result: ERROR
     Running ‘run-all.R’ [14s/21s]
    Running the tests in ‘tests/run-all.R’ failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     ── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     ── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     ── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.4.8
Check: tests
Result: ERROR
     Running ‘run-all.R’ [24s/25s]
    Running the tests in ‘tests/run-all.R’ failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     ── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     ── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     ── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.4.8
Check: tests
Result: ERROR
     Running ‘run-all.R’ [23s/25s]
    Running the tests in ‘tests/run-all.R’ failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     ── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     ── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     ── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 1.4.8
Check: tests
Result: ERROR
     Running 'run-all.R' [19s]
    Running the tests in 'tests/run-all.R' failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     -- 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     -- 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     -- 3. Failure: thresholdIdentification works (@tests.R#93) --------------------
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     == testthat results ===========================================================
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-devel-windows-ix86+x86_64

Version: 1.4.8
Check: tests
Result: ERROR
     Running ‘run-all.R’ [19s/21s]
    Running the tests in ‘tests/run-all.R’ failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     ── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     ── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     ── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-linux-x86_64

Version: 1.4.8
Check: tests
Result: ERROR
     Running ‘run-all.R’ [36s/55s]
    Running the tests in ‘tests/run-all.R’ failed.
    Complete output:
     > library(testthat)
     > library(fanovaGraph)
     Loading required package: sensitivity
     Loading required package: igraph
    
     Attaching package: 'igraph'
    
     The following object is masked from 'package:testthat':
    
     compare
    
     The following objects are masked from 'package:stats':
    
     decompose, spectrum
    
     The following object is masked from 'package:base':
    
     union
    
     Loading required package: DiceKriging
     >
     > test_check("fanovaGraph")
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 3.975224 3.967393 3.977366 3.946013 3.935601 3.983994
     - best initial criterion value(s) : -26.60656
    
     N = 6, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= 26.607 |proj g|= 3.0822
     At iterate 1 f = 20.74 |proj g|= 3.6659
     At iterate 2 f = 14.479 |proj g|= 2.4267
     At iterate 3 f = 12.836 |proj g|= 0.96942
     At iterate 4 f = 12.753 |proj g|= 0.39588
     At iterate 5 f = 12.729 |proj g|= 0.20836
     At iterate 6 f = 12.724 |proj g|= 0.14199
     At iterate 7 f = 12.72 |proj g|= 0.032266
     At iterate 8 f = 12.72 |proj g|= 0.013542
     At iterate 9 f = 12.72 |proj g|= 0.0062952
     At iterate 10 f = 12.72 |proj g|= 0.001052
     At iterate 11 f = 12.72 |proj g|= 0.0003525
     At iterate 12 f = 12.72 |proj g|= 0.00011112
    
     iterations 12
     function evaluations 14
     segments explored during Cauchy searches 16
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 0.000111123
     final function value 12.7196
    
     F = 12.7196
     final value 12.719557
     converged
     ── 1. Failure: estimateGraph works for different n.tot and d for all methods (@t
     -60.9438209 not equivalent to ...[].
     1/1 mismatches
     [1] -60.9 - -85.3 == 24.3
    
     ── 2. Failure: estimateGraph works for different n.tot and d for all methods (@t
     0.008837558 not equivalent to ...[].
     1/1 mismatches
     [1] 0.00884 - 0.00829 == 0.000549
    
    
     optimisation start
     ------------------
     * estimation method : MLE
     * optimisation method : BFGS
     * analytical gradient : used
     * trend model : ~1
     * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10
     - parameters upper bounds : 2 2 2
     - best initial criterion value(s) : 8.196933
    
     N = 3, M = 5 machine precision = 2.22045e-16
     At X0, 0 variables are exactly at the bounds
     At iterate 0 f= -8.1969 |proj g|= 1.2609
     At iterate 1 f = -8.5263 |proj g|= 1.1046
     At iterate 2 f = -8.584 |proj g|= 0.5739
     At iterate 3 f = -8.6328 |proj g|= 0.81099
     At iterate 4 f = -8.7399 |proj g|= 0.84982
     At iterate 5 f = -8.7886 |proj g|= 0.24597
     At iterate 6 f = -8.7949 |proj g|= 0.055409
     At iterate 7 f = -8.7955 |proj g|= 0.18664
     At iterate 8 f = -8.7958 |proj g|= 0.013326
     At iterate 9 f = -8.7958 |proj g|= 0.00073279
     At iterate 10 f = -8.7958 |proj g|= 4.7913e-05
     At iterate 11 f = -8.7958 |proj g|= 3.9342e-05
    
     iterations 11
     function evaluations 14
     segments explored during Cauchy searches 13
     BFGS updates skipped 0
     active bounds at final generalized Cauchy point 0
     norm of the final projected gradient 3.93423e-05
     final function value -8.79581
    
     F = -8.79581
     final value -8.795814
     converged
     threshold RMSE
     [1,] 0.0 0.6598290
     [2,] 0.4 0.2384378
     [3,] 1.0 1.0152007
     ── 3. Failure: thresholdIdentification works (@tests.R#93) ────────────────────
     c(0.328177717247, 0.218761863491, 0.99079526514) not equivalent to comparison$RMSE.
     3/3 mismatches (average diff: 0.125)
     [1] 0.328 - 0.660 == -0.3317
     [2] 0.219 - 0.238 == -0.0197
     [3] 0.991 - 1.015 == -0.0244
    
     ══ testthat results ═══════════════════════════════════════════════════════════
     OK: 29 SKIPPED: 0 FAILED: 3
     1. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#26)
     2. Failure: estimateGraph works for different n.tot and d for all methods (@tests.R#30)
     3. Failure: thresholdIdentification works (@tests.R#93)
    
     Error: testthat unit tests failed
     Execution halted
Flavor: r-patched-solaris-x86