##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HPiP.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HPiP_1.15.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck’
* using R version 4.5.1 Patched (2025-09-10 r88807)
* using platform: aarch64-apple-darwin20
* R was compiled by
    Apple clang version 16.0.0 (clang-1600.0.26.6)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Ventura 13.7.7
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.15.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       17.930  0.708  18.655
FSmethod      17.558  0.674  18.454
corr_plot     17.265  0.659  18.132
pred_ensembel  5.743  0.099   5.234
enrichfindP    0.163  0.029   7.469
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/Users/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.15.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R version 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> BiocGenerics:::testPackage('HPiP')
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
No results to show
Please make sure that the organism is correct or set significant = FALSE
avNNet
Loading required package: ggplot2
Loading required package: lattice
Fitting Repeat 1 

# weights:  103
initial  value 100.665600 
iter  10 value 94.362736
iter  20 value 93.875306
final  value 93.874286 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.992714 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.452901 
iter  10 value 94.264963
iter  20 value 93.922942
iter  30 value 93.922241
iter  40 value 93.722223
iter  40 value 93.722222
iter  40 value 93.722222
final  value 93.722222 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.466604 
final  value 94.400000 
converged
Fitting Repeat 5 

# weights:  103
initial  value 108.409433 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 113.368381 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.264886 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 111.929425 
iter  10 value 88.931941
iter  20 value 86.864253
iter  30 value 86.273978
final  value 86.270960 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.911324 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.798139 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 100.835718 
iter  10 value 94.340758
iter  20 value 94.340406
final  value 94.340398 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.755388 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 103.463065 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 115.815893 
iter  10 value 93.999909
iter  20 value 87.792290
iter  30 value 87.688184
final  value 87.687921 
converged
Fitting Repeat 5 

# weights:  507
initial  value 131.860889 
final  value 94.443244 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.710728 
iter  10 value 94.495282
iter  20 value 92.087675
iter  30 value 88.138403
iter  40 value 86.443340
iter  50 value 85.838643
iter  60 value 85.476292
iter  70 value 84.776181
iter  80 value 84.489944
iter  90 value 84.268949
iter 100 value 83.912800
final  value 83.912800 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 99.452939 
iter  10 value 92.150427
iter  20 value 89.495837
iter  30 value 86.335288
iter  40 value 85.656300
iter  50 value 84.864627
iter  60 value 83.778613
iter  70 value 83.530077
final  value 83.528296 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.347646 
iter  10 value 94.449928
iter  20 value 89.321235
iter  30 value 88.608383
iter  40 value 88.487312
iter  50 value 85.872689
iter  60 value 84.601384
iter  70 value 84.210723
iter  80 value 84.040237
iter  90 value 83.898350
iter 100 value 83.686058
final  value 83.686058 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 109.058661 
iter  10 value 94.486424
iter  20 value 94.329328
iter  30 value 92.056564
iter  40 value 86.321335
iter  50 value 85.872964
iter  60 value 85.715250
iter  70 value 85.127057
iter  80 value 84.255123
iter  90 value 84.089425
iter 100 value 83.830999
final  value 83.830999 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 97.516001 
iter  10 value 94.483110
iter  20 value 94.250157
iter  30 value 94.239074
iter  40 value 92.694236
iter  50 value 88.195139
iter  60 value 87.846413
iter  70 value 87.718972
iter  80 value 87.607902
iter  90 value 87.165022
iter 100 value 86.509989
final  value 86.509989 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 123.625405 
iter  10 value 94.453358
iter  20 value 88.615693
iter  30 value 87.231521
iter  40 value 86.086757
iter  50 value 84.827983
iter  60 value 84.344295
iter  70 value 83.973906
iter  80 value 83.091750
iter  90 value 82.831795
iter 100 value 82.717312
final  value 82.717312 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.674026 
iter  10 value 94.509494
iter  20 value 94.290439
iter  30 value 93.497437
iter  40 value 88.225261
iter  50 value 87.613152
iter  60 value 86.008944
iter  70 value 85.064527
iter  80 value 84.020638
iter  90 value 83.665910
iter 100 value 83.321369
final  value 83.321369 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 118.689714 
iter  10 value 96.342818
iter  20 value 90.058087
iter  30 value 86.959670
iter  40 value 85.282101
iter  50 value 84.172156
iter  60 value 83.946055
iter  70 value 83.893553
iter  80 value 83.830106
iter  90 value 83.754963
iter 100 value 83.603124
final  value 83.603124 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.225263 
iter  10 value 94.476669
iter  20 value 93.825521
iter  30 value 87.962228
iter  40 value 85.891847
iter  50 value 85.601264
iter  60 value 85.347764
iter  70 value 84.869528
iter  80 value 83.263007
iter  90 value 82.562673
iter 100 value 82.386560
final  value 82.386560 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.415168 
iter  10 value 94.491445
final  value 94.486180 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.650836 
iter  10 value 94.498379
iter  20 value 93.937412
iter  30 value 92.805822
iter  40 value 89.881978
iter  50 value 86.112630
iter  60 value 84.552557
iter  70 value 83.436552
iter  80 value 82.523287
iter  90 value 81.920454
iter 100 value 81.736943
final  value 81.736943 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.420244 
iter  10 value 95.018509
iter  20 value 92.023340
iter  30 value 88.882336
iter  40 value 86.643855
iter  50 value 85.779934
iter  60 value 85.150320
iter  70 value 84.973006
iter  80 value 83.320261
iter  90 value 82.268466
iter 100 value 82.028448
final  value 82.028448 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.499277 
iter  10 value 95.008214
iter  20 value 91.349253
iter  30 value 86.102372
iter  40 value 84.831221
iter  50 value 84.616089
iter  60 value 84.032018
iter  70 value 83.451677
iter  80 value 82.528937
iter  90 value 82.404659
iter 100 value 82.228405
final  value 82.228405 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 118.436583 
iter  10 value 94.258566
iter  20 value 89.661229
iter  30 value 84.818672
iter  40 value 84.195565
iter  50 value 83.670130
iter  60 value 83.397391
iter  70 value 82.819197
iter  80 value 82.624077
iter  90 value 82.609271
iter 100 value 82.595957
final  value 82.595957 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.957177 
iter  10 value 94.511711
iter  20 value 92.572725
iter  30 value 89.478839
iter  40 value 87.316977
iter  50 value 85.436417
iter  60 value 84.415806
iter  70 value 83.866647
iter  80 value 83.676847
iter  90 value 83.453426
iter 100 value 83.367222
final  value 83.367222 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.516276 
iter  10 value 94.486043
iter  20 value 94.484227
final  value 94.484220 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.686201 
iter  10 value 94.485738
iter  20 value 94.484194
iter  30 value 94.215681
final  value 94.214121 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.266850 
final  value 94.485489 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.049420 
iter  10 value 94.486032
iter  20 value 94.478528
iter  30 value 88.882394
iter  40 value 88.407541
iter  50 value 88.380969
iter  60 value 87.821116
iter  70 value 87.687886
iter  80 value 87.685947
iter  90 value 87.024581
final  value 86.834674 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.213292 
final  value 94.486018 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.214540 
iter  10 value 94.487885
iter  20 value 94.092184
iter  30 value 88.582630
iter  40 value 87.867951
iter  50 value 87.509773
iter  60 value 86.876960
iter  70 value 86.869250
final  value 86.869162 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.589520 
iter  10 value 94.158336
iter  20 value 89.011243
iter  30 value 88.766520
iter  40 value 88.766112
final  value 88.765517 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.655791 
iter  10 value 94.448173
iter  20 value 94.412134
final  value 94.263506 
converged
Fitting Repeat 4 

# weights:  305
initial  value 112.622585 
iter  10 value 94.448188
iter  20 value 89.044912
iter  30 value 87.183630
iter  40 value 87.070622
iter  50 value 87.068191
iter  60 value 86.602012
iter  70 value 86.567555
final  value 86.567526 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.032799 
iter  10 value 94.399697
iter  20 value 94.327760
iter  30 value 94.323194
iter  40 value 93.393930
iter  50 value 88.687697
iter  60 value 84.944379
iter  70 value 83.157369
iter  80 value 82.260708
iter  90 value 82.253912
iter 100 value 82.250925
final  value 82.250925 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.525119 
iter  10 value 94.492430
iter  20 value 94.438357
iter  30 value 87.426311
final  value 87.317799 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.567583 
iter  10 value 94.451937
iter  20 value 94.398976
iter  30 value 94.272035
iter  40 value 94.263877
iter  50 value 94.263342
iter  50 value 94.263341
iter  50 value 94.263341
final  value 94.263341 
converged
Fitting Repeat 3 

# weights:  507
initial  value 126.434393 
iter  10 value 94.492402
iter  20 value 94.244404
iter  30 value 88.813047
iter  40 value 86.004654
iter  50 value 84.959974
final  value 84.904398 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.306128 
iter  10 value 94.179866
iter  20 value 94.174898
iter  30 value 94.170163
iter  40 value 94.139109
iter  50 value 94.127352
iter  60 value 89.071602
iter  70 value 89.011089
iter  80 value 86.114841
iter  90 value 86.016035
iter 100 value 85.993949
final  value 85.993949 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 102.758332 
iter  10 value 94.408520
iter  20 value 94.407136
iter  30 value 94.067670
iter  40 value 88.221250
iter  50 value 87.437827
iter  60 value 87.285181
iter  70 value 84.046211
iter  80 value 83.149276
iter  90 value 82.066766
iter 100 value 81.089967
final  value 81.089967 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.776450 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 111.088040 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 110.244683 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 94.516953 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.134259 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.249929 
iter  10 value 94.026551
iter  10 value 94.026550
iter  10 value 94.026550
final  value 94.026550 
converged
Fitting Repeat 2 

# weights:  305
initial  value 114.416422 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.589414 
iter  10 value 94.372171
iter  20 value 91.815259
final  value 91.815245 
converged
Fitting Repeat 4 

# weights:  305
initial  value 110.269121 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.534240 
iter  10 value 93.671150
iter  20 value 91.425661
final  value 91.122771 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.657698 
final  value 94.026542 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.905196 
iter  10 value 94.026543
iter  10 value 94.026542
iter  10 value 94.026542
final  value 94.026542 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.173391 
iter  10 value 93.894017
final  value 93.893997 
converged
Fitting Repeat 4 

# weights:  507
initial  value 121.198293 
iter  10 value 87.454257
iter  20 value 85.971972
iter  30 value 85.667907
iter  40 value 84.801611
iter  50 value 84.447902
iter  60 value 84.437295
iter  70 value 84.432581
iter  80 value 84.427000
iter  90 value 84.426702
final  value 84.426681 
converged
Fitting Repeat 5 

# weights:  507
initial  value 118.443704 
final  value 94.026542 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.287791 
iter  10 value 94.488558
iter  10 value 94.488557
iter  10 value 94.488557
final  value 94.488557 
converged
Fitting Repeat 2 

# weights:  103
initial  value 107.356209 
iter  10 value 94.418793
iter  20 value 91.554974
iter  30 value 91.370958
iter  40 value 86.102755
iter  50 value 84.605775
iter  60 value 83.485949
iter  70 value 83.054355
iter  80 value 82.651870
iter  90 value 82.554347
final  value 82.554165 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.478246 
iter  10 value 94.475093
iter  20 value 89.825712
iter  30 value 85.892187
iter  40 value 85.130283
iter  50 value 84.436364
iter  60 value 84.024363
iter  70 value 82.880526
iter  80 value 82.811030
iter  90 value 82.801627
iter 100 value 82.792527
final  value 82.792527 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 108.785719 
iter  10 value 94.397969
iter  20 value 93.041583
iter  30 value 89.127729
iter  40 value 86.584629
iter  50 value 85.985875
iter  60 value 84.088997
iter  70 value 83.414444
iter  80 value 82.941169
iter  90 value 82.558548
iter 100 value 82.554169
final  value 82.554169 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 98.518036 
iter  10 value 94.486528
iter  20 value 88.007500
iter  30 value 84.976218
iter  40 value 84.404129
iter  50 value 84.310697
iter  60 value 84.305213
iter  60 value 84.305212
iter  60 value 84.305212
final  value 84.305212 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.295704 
iter  10 value 94.399565
iter  20 value 86.541745
iter  30 value 85.345612
iter  40 value 84.386327
iter  50 value 83.687519
iter  60 value 82.140355
iter  70 value 81.465310
iter  80 value 81.336705
iter  90 value 81.105527
iter 100 value 81.008260
final  value 81.008260 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 126.810407 
iter  10 value 93.147880
iter  20 value 85.786400
iter  30 value 85.022857
iter  40 value 84.428853
iter  50 value 84.001325
iter  60 value 83.777583
iter  70 value 83.170658
iter  80 value 82.241294
iter  90 value 81.813617
iter 100 value 81.571884
final  value 81.571884 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.617114 
iter  10 value 94.039062
iter  20 value 86.466985
iter  30 value 85.006274
iter  40 value 84.358910
iter  50 value 84.271578
iter  60 value 83.413280
iter  70 value 83.262151
iter  80 value 83.083771
iter  90 value 82.932872
iter 100 value 82.911954
final  value 82.911954 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 117.949925 
iter  10 value 94.504067
iter  20 value 91.007280
iter  30 value 85.263173
iter  40 value 84.468930
iter  50 value 84.029085
iter  60 value 83.217936
iter  70 value 82.700670
iter  80 value 82.002997
iter  90 value 81.884113
iter 100 value 81.764471
final  value 81.764471 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.006376 
iter  10 value 94.283982
iter  20 value 87.240616
iter  30 value 85.490447
iter  40 value 84.718787
iter  50 value 84.337134
iter  60 value 84.066743
iter  70 value 83.215392
iter  80 value 82.249105
iter  90 value 82.161831
iter 100 value 82.002516
final  value 82.002516 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 126.927065 
iter  10 value 94.533348
iter  20 value 92.013378
iter  30 value 91.334940
iter  40 value 89.884100
iter  50 value 84.780832
iter  60 value 84.451828
iter  70 value 84.090181
iter  80 value 83.946989
iter  90 value 83.504247
iter 100 value 82.584778
final  value 82.584778 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.608099 
iter  10 value 94.114450
iter  20 value 89.829236
iter  30 value 86.518664
iter  40 value 85.856932
iter  50 value 84.250641
iter  60 value 83.778575
iter  70 value 83.375652
iter  80 value 82.049552
iter  90 value 81.510773
iter 100 value 81.377933
final  value 81.377933 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 117.050716 
iter  10 value 94.542624
iter  20 value 92.356264
iter  30 value 84.800804
iter  40 value 82.990042
iter  50 value 82.350848
iter  60 value 81.961842
iter  70 value 81.566825
iter  80 value 81.365243
iter  90 value 81.232453
iter 100 value 81.184871
final  value 81.184871 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.587893 
iter  10 value 94.248208
iter  20 value 87.498958
iter  30 value 84.627290
iter  40 value 83.179985
iter  50 value 82.553668
iter  60 value 81.346657
iter  70 value 81.050297
iter  80 value 80.867175
iter  90 value 80.784210
iter 100 value 80.721654
final  value 80.721654 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.655032 
iter  10 value 94.542098
iter  20 value 86.564198
iter  30 value 85.609615
iter  40 value 84.600156
iter  50 value 83.951682
iter  60 value 83.352496
iter  70 value 82.316257
iter  80 value 81.676501
iter  90 value 81.365075
iter 100 value 81.135376
final  value 81.135376 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.576300 
iter  10 value 94.327704
iter  20 value 94.028444
iter  30 value 94.026898
final  value 94.026859 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.746778 
iter  10 value 94.485907
final  value 94.485906 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.898073 
final  value 94.485816 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.329629 
final  value 94.485846 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.824739 
iter  10 value 94.486089
final  value 94.484222 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.276414 
iter  10 value 94.489475
iter  20 value 94.473452
iter  30 value 93.916559
iter  40 value 91.508173
iter  50 value 91.466007
iter  60 value 91.465576
iter  70 value 91.465162
final  value 91.464946 
converged
Fitting Repeat 2 

# weights:  305
initial  value 111.155356 
iter  10 value 94.031975
iter  20 value 94.027658
iter  30 value 93.265903
iter  40 value 85.528759
iter  50 value 84.839507
iter  60 value 84.749292
iter  70 value 84.341301
iter  80 value 84.199898
iter  90 value 84.180646
iter 100 value 83.561601
final  value 83.561601 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 96.449451 
iter  10 value 94.031880
iter  20 value 94.027250
iter  30 value 94.025464
iter  40 value 89.823832
iter  50 value 88.818067
iter  60 value 88.817969
iter  60 value 88.817969
iter  60 value 88.817969
final  value 88.817969 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.295371 
iter  10 value 94.488857
iter  20 value 94.436695
iter  30 value 93.830216
iter  40 value 91.174277
iter  50 value 91.155672
iter  60 value 91.153122
iter  70 value 91.152911
iter  80 value 91.125587
final  value 91.120625 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.574936 
iter  10 value 94.037117
iter  20 value 94.031095
iter  30 value 94.026711
iter  40 value 93.448483
iter  50 value 89.732195
iter  60 value 86.891206
iter  70 value 86.745019
iter  80 value 86.744310
iter  90 value 85.492256
iter 100 value 84.324170
final  value 84.324170 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 117.659005 
iter  10 value 88.457684
iter  20 value 85.013650
iter  30 value 84.916165
iter  40 value 84.663352
iter  50 value 83.975998
iter  60 value 83.676237
iter  70 value 83.651797
iter  80 value 83.651030
iter  90 value 82.961885
iter 100 value 82.316160
final  value 82.316160 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 134.262150 
iter  10 value 94.493505
iter  20 value 94.486760
iter  30 value 92.253799
iter  40 value 88.357313
iter  50 value 88.292424
iter  60 value 88.087107
iter  70 value 86.954432
iter  80 value 86.905723
iter  90 value 83.705753
iter 100 value 82.826397
final  value 82.826397 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 100.403428 
iter  10 value 92.789529
iter  20 value 86.996467
iter  30 value 86.956530
iter  40 value 86.951437
iter  50 value 86.105966
iter  60 value 84.861879
iter  70 value 81.927384
iter  80 value 81.914404
iter  90 value 81.914305
iter  90 value 81.914304
iter  90 value 81.914304
final  value 81.914304 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.308278 
iter  10 value 94.491922
iter  20 value 93.074661
iter  30 value 83.715412
final  value 83.670347 
converged
Fitting Repeat 5 

# weights:  507
initial  value 108.125156 
iter  10 value 94.122708
iter  20 value 93.950804
iter  30 value 92.134975
iter  40 value 91.567072
iter  50 value 91.566480
iter  60 value 91.255130
final  value 91.191617 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.581680 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.491231 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.406484 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.815368 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 110.006587 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.531344 
final  value 94.466823 
converged
Fitting Repeat 2 

# weights:  305
initial  value 105.004587 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 108.967122 
iter  10 value 91.310278
iter  20 value 91.070900
iter  30 value 91.064094
final  value 91.064092 
converged
Fitting Repeat 4 

# weights:  305
initial  value 109.344861 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 110.769256 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 109.179903 
final  value 94.466823 
converged
Fitting Repeat 2 

# weights:  507
initial  value 134.231706 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 113.017732 
iter  10 value 93.637386
iter  10 value 93.637386
iter  10 value 93.637386
final  value 93.637386 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.136765 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 119.758861 
final  value 94.088889 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.825901 
iter  10 value 92.762755
iter  20 value 91.711823
iter  30 value 88.835465
iter  40 value 85.494512
iter  50 value 84.867105
iter  60 value 82.908348
iter  70 value 81.999584
iter  80 value 81.530340
iter  90 value 81.525931
final  value 81.524475 
converged
Fitting Repeat 2 

# weights:  103
initial  value 112.804868 
iter  10 value 94.442309
iter  20 value 92.588066
iter  30 value 90.434851
iter  40 value 87.402173
iter  50 value 85.349892
iter  60 value 82.385316
iter  70 value 81.632280
iter  80 value 81.540184
iter  90 value 81.530373
iter 100 value 81.529267
final  value 81.529267 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.625404 
iter  10 value 94.461024
iter  20 value 89.124208
iter  30 value 85.194995
iter  40 value 83.291702
iter  50 value 82.836703
iter  60 value 82.076183
iter  70 value 81.699682
iter  80 value 81.677435
iter  90 value 81.666903
final  value 81.666870 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.164044 
iter  10 value 94.426870
iter  20 value 93.584201
iter  30 value 93.393411
iter  40 value 88.652881
iter  50 value 86.061033
iter  60 value 84.995361
iter  70 value 82.572088
iter  80 value 82.364756
iter  90 value 82.211576
iter 100 value 81.545750
final  value 81.545750 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 106.929727 
iter  10 value 94.453773
iter  20 value 94.036847
iter  30 value 92.729410
iter  40 value 92.634194
iter  50 value 91.470263
iter  60 value 86.758597
iter  70 value 86.001749
iter  80 value 84.532472
iter  90 value 82.295865
iter 100 value 81.974171
final  value 81.974171 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 112.981245 
iter  10 value 94.117644
iter  20 value 89.179425
iter  30 value 86.503864
iter  40 value 84.662088
iter  50 value 83.452765
iter  60 value 82.398802
iter  70 value 82.006581
iter  80 value 81.960495
iter  90 value 81.792688
iter 100 value 81.513486
final  value 81.513486 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.774111 
iter  10 value 96.074493
iter  20 value 88.303341
iter  30 value 87.532087
iter  40 value 86.698595
iter  50 value 84.717578
iter  60 value 82.657453
iter  70 value 81.616918
iter  80 value 81.535305
iter  90 value 81.380953
iter 100 value 81.337783
final  value 81.337783 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.994045 
iter  10 value 94.499643
iter  20 value 92.795196
iter  30 value 88.717908
iter  40 value 85.977778
iter  50 value 84.013714
iter  60 value 82.981114
iter  70 value 81.944616
iter  80 value 81.472935
iter  90 value 81.402617
iter 100 value 81.169341
final  value 81.169341 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.324937 
iter  10 value 96.289339
iter  20 value 94.709881
iter  30 value 86.989619
iter  40 value 84.533620
iter  50 value 83.699874
iter  60 value 82.368140
iter  70 value 81.844741
iter  80 value 81.832000
iter  90 value 81.721239
iter 100 value 81.061756
final  value 81.061756 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.845321 
iter  10 value 94.114968
iter  20 value 87.460939
iter  30 value 86.897516
iter  40 value 85.356733
iter  50 value 83.647612
iter  60 value 83.346393
iter  70 value 82.957174
iter  80 value 82.210162
iter  90 value 81.562427
iter 100 value 80.575637
final  value 80.575637 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.144600 
iter  10 value 94.658673
iter  20 value 94.450723
iter  30 value 93.453395
iter  40 value 89.432076
iter  50 value 87.287210
iter  60 value 85.628733
iter  70 value 85.129652
iter  80 value 82.013680
iter  90 value 81.679629
iter 100 value 81.402037
final  value 81.402037 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 114.739676 
iter  10 value 91.992017
iter  20 value 89.269357
iter  30 value 86.800726
iter  40 value 86.442965
iter  50 value 83.340059
iter  60 value 82.495240
iter  70 value 82.006501
iter  80 value 81.530703
iter  90 value 81.243119
iter 100 value 81.042524
final  value 81.042524 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 108.094347 
iter  10 value 94.361120
iter  20 value 89.850594
iter  30 value 87.508168
iter  40 value 86.600960
iter  50 value 86.393117
iter  60 value 85.829317
iter  70 value 85.426048
iter  80 value 84.444785
iter  90 value 82.432522
iter 100 value 81.055073
final  value 81.055073 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.034611 
iter  10 value 94.555646
iter  20 value 93.279990
iter  30 value 87.187320
iter  40 value 86.400063
iter  50 value 84.833925
iter  60 value 82.777449
iter  70 value 81.990567
iter  80 value 81.135728
iter  90 value 80.523295
iter 100 value 80.309828
final  value 80.309828 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.075176 
iter  10 value 95.702594
iter  20 value 94.492300
iter  30 value 91.418757
iter  40 value 87.930647
iter  50 value 85.090241
iter  60 value 84.781072
iter  70 value 84.602042
iter  80 value 84.296655
iter  90 value 82.422004
iter 100 value 81.871885
final  value 81.871885 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.393973 
final  value 94.486092 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.473230 
final  value 94.485820 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.797887 
final  value 94.485754 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.769415 
final  value 94.468571 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.609731 
final  value 94.485849 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.263702 
iter  10 value 94.471387
iter  20 value 94.329974
iter  30 value 87.548401
iter  40 value 87.546620
iter  50 value 87.123125
iter  60 value 87.074453
iter  70 value 87.073997
iter  80 value 86.402649
iter  90 value 84.602938
iter 100 value 83.767107
final  value 83.767107 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 113.016582 
iter  10 value 94.489532
iter  20 value 94.431090
iter  30 value 86.629469
final  value 85.920367 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.169901 
iter  10 value 94.489074
iter  20 value 94.435406
iter  30 value 89.049140
iter  40 value 86.618808
iter  50 value 86.349532
iter  60 value 85.807581
iter  70 value 85.442053
iter  80 value 85.209874
iter  90 value 85.207500
final  value 85.207497 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.633956 
iter  10 value 94.433136
iter  20 value 94.428641
iter  30 value 94.427782
iter  40 value 88.382481
iter  50 value 88.378922
iter  60 value 88.372733
iter  70 value 88.356020
final  value 88.323198 
converged
Fitting Repeat 5 

# weights:  305
initial  value 109.078411 
iter  10 value 93.881651
iter  20 value 93.822013
iter  30 value 93.770173
final  value 93.766461 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.039797 
iter  10 value 94.474998
final  value 94.434768 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.044394 
iter  10 value 92.156782
iter  20 value 91.090823
iter  30 value 91.089735
iter  40 value 91.072169
iter  50 value 91.022974
iter  60 value 91.022061
iter  70 value 91.016088
iter  80 value 91.015509
iter  90 value 90.971693
iter 100 value 90.777825
final  value 90.777825 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.605590 
iter  10 value 94.488847
iter  20 value 93.248080
iter  30 value 85.380081
iter  40 value 84.915787
iter  50 value 84.915456
iter  60 value 84.915169
iter  70 value 83.769317
iter  80 value 83.120769
iter  90 value 82.992510
iter 100 value 80.860910
final  value 80.860910 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 135.349674 
iter  10 value 94.119078
iter  20 value 94.095525
iter  30 value 92.123258
iter  40 value 88.453862
iter  50 value 88.223627
iter  60 value 88.169520
iter  70 value 87.920215
iter  80 value 87.780369
final  value 87.780169 
converged
Fitting Repeat 5 

# weights:  507
initial  value 104.162475 
iter  10 value 92.103843
iter  20 value 90.772278
iter  30 value 90.506976
iter  40 value 89.332858
iter  50 value 89.200860
iter  60 value 89.198840
final  value 89.198049 
converged
Fitting Repeat 1 

# weights:  103
initial  value 118.647505 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.538582 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.743015 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.269538 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.960969 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.682912 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.545568 
final  value 94.044524 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.788057 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.827549 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.625410 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 114.157084 
iter  10 value 91.347736
iter  20 value 90.049232
iter  30 value 90.048733
iter  30 value 90.048733
iter  30 value 90.048733
final  value 90.048733 
converged
Fitting Repeat 2 

# weights:  507
initial  value 101.165204 
iter  10 value 93.582418
iter  10 value 93.582418
iter  10 value 93.582418
final  value 93.582418 
converged
Fitting Repeat 3 

# weights:  507
initial  value 109.742464 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.169521 
final  value 93.869756 
converged
Fitting Repeat 5 

# weights:  507
initial  value 121.382835 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 107.385720 
iter  10 value 94.059029
iter  20 value 88.222958
iter  30 value 86.158658
iter  40 value 84.796230
iter  50 value 84.271941
iter  60 value 82.069648
iter  70 value 81.824103
iter  80 value 81.815857
iter  90 value 81.814583
iter 100 value 81.814400
final  value 81.814400 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.861125 
iter  10 value 94.056693
iter  20 value 93.715557
iter  30 value 87.073626
iter  40 value 82.360896
iter  50 value 81.564208
iter  60 value 81.032849
iter  70 value 80.621898
iter  80 value 79.961818
iter  90 value 79.937079
iter 100 value 79.922088
final  value 79.922088 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 100.582038 
iter  10 value 94.057094
iter  20 value 93.720027
iter  30 value 93.685033
iter  40 value 90.700825
iter  50 value 84.792359
iter  60 value 80.865500
iter  70 value 80.773797
iter  80 value 80.743380
iter  90 value 80.587259
iter 100 value 80.464705
final  value 80.464705 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.771871 
iter  10 value 94.059778
iter  20 value 92.619410
iter  30 value 87.223528
iter  40 value 85.892797
iter  50 value 85.577782
iter  60 value 85.268450
iter  70 value 85.253667
iter  80 value 85.227990
iter  90 value 83.332390
iter 100 value 82.414487
final  value 82.414487 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.618694 
iter  10 value 94.048160
iter  20 value 85.698277
iter  30 value 82.349186
iter  40 value 82.329685
iter  50 value 82.319543
iter  50 value 82.319543
final  value 82.319543 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.322809 
iter  10 value 93.281296
iter  20 value 87.096660
iter  30 value 84.850239
iter  40 value 82.601701
iter  50 value 81.859342
iter  60 value 81.616686
iter  70 value 80.925875
iter  80 value 80.623825
iter  90 value 80.200933
iter 100 value 79.921238
final  value 79.921238 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.923709 
iter  10 value 94.108426
iter  20 value 94.061266
iter  30 value 93.688506
iter  40 value 87.192809
iter  50 value 83.652835
iter  60 value 82.761601
iter  70 value 81.790597
iter  80 value 81.265664
iter  90 value 80.707188
iter 100 value 80.145130
final  value 80.145130 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.381443 
iter  10 value 93.721584
iter  20 value 85.107778
iter  30 value 83.614336
iter  40 value 82.995570
iter  50 value 82.948833
iter  60 value 82.806726
iter  70 value 82.578349
iter  80 value 82.330020
iter  90 value 82.104906
iter 100 value 82.068290
final  value 82.068290 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.687611 
iter  10 value 91.783845
iter  20 value 86.684643
iter  30 value 85.871604
iter  40 value 85.026696
iter  50 value 83.259465
iter  60 value 82.888697
iter  70 value 81.843710
iter  80 value 81.206807
iter  90 value 80.113915
iter 100 value 79.159469
final  value 79.159469 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 123.555617 
iter  10 value 94.083854
iter  20 value 90.351545
iter  30 value 83.148453
iter  40 value 81.720599
iter  50 value 79.575116
iter  60 value 78.379827
iter  70 value 78.253807
iter  80 value 78.047426
iter  90 value 77.661662
iter 100 value 77.571064
final  value 77.571064 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 116.999548 
iter  10 value 93.402431
iter  20 value 88.581978
iter  30 value 84.452955
iter  40 value 82.325882
iter  50 value 81.722713
iter  60 value 81.126787
iter  70 value 79.378357
iter  80 value 78.859175
iter  90 value 78.146641
iter 100 value 77.965058
final  value 77.965058 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 117.260629 
iter  10 value 94.121004
iter  20 value 89.832306
iter  30 value 88.370695
iter  40 value 83.800877
iter  50 value 82.637745
iter  60 value 82.396376
iter  70 value 81.360974
iter  80 value 80.872896
iter  90 value 80.236048
iter 100 value 79.989688
final  value 79.989688 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 129.737120 
iter  10 value 93.761785
iter  20 value 89.464925
iter  30 value 89.070341
iter  40 value 86.615087
iter  50 value 83.009953
iter  60 value 80.067167
iter  70 value 79.688586
iter  80 value 78.659637
iter  90 value 78.094935
iter 100 value 78.002377
final  value 78.002377 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 117.170352 
iter  10 value 94.079564
iter  20 value 93.937948
iter  30 value 93.463754
iter  40 value 93.417476
iter  50 value 92.885550
iter  60 value 83.988518
iter  70 value 81.272861
iter  80 value 79.715956
iter  90 value 78.952025
iter 100 value 78.267657
final  value 78.267657 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 128.113237 
iter  10 value 93.947592
iter  20 value 86.846239
iter  30 value 82.640189
iter  40 value 80.183590
iter  50 value 79.034531
iter  60 value 78.733121
iter  70 value 78.363590
iter  80 value 78.018993
iter  90 value 77.933818
iter 100 value 77.894048
final  value 77.894048 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.807106 
final  value 94.054694 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.894698 
iter  10 value 93.584376
iter  20 value 93.382972
final  value 93.366568 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.107249 
iter  10 value 93.018926
iter  20 value 91.085190
iter  30 value 91.070489
iter  40 value 91.069994
iter  50 value 91.068020
iter  60 value 90.166486
iter  70 value 83.746542
iter  80 value 83.007252
iter  90 value 82.791984
iter 100 value 82.660489
final  value 82.660489 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 98.388208 
final  value 94.054943 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.000721 
final  value 94.054605 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.397178 
iter  10 value 93.587399
iter  20 value 93.575815
iter  30 value 93.130002
iter  40 value 85.130284
iter  50 value 83.172566
iter  60 value 83.149088
final  value 83.148943 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.772795 
iter  10 value 93.383762
iter  20 value 93.370849
iter  30 value 93.365375
iter  40 value 87.407399
iter  50 value 85.561356
iter  60 value 84.721431
iter  70 value 84.604639
final  value 84.603766 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.377776 
iter  10 value 94.054104
iter  20 value 94.003284
iter  30 value 85.762019
iter  40 value 85.389634
iter  50 value 85.388306
iter  60 value 85.386871
final  value 85.386313 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.418170 
iter  10 value 94.032099
iter  20 value 93.533178
iter  30 value 93.417274
iter  40 value 93.305197
iter  50 value 93.302287
iter  60 value 93.301437
final  value 93.301012 
converged
Fitting Repeat 5 

# weights:  305
initial  value 107.880370 
iter  10 value 93.415220
iter  20 value 93.409437
iter  30 value 93.382071
iter  40 value 93.358649
iter  50 value 93.301614
final  value 93.301603 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.882075 
iter  10 value 93.462955
iter  10 value 93.462955
final  value 93.462955 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.119781 
iter  10 value 93.883721
iter  20 value 93.877838
iter  30 value 93.870565
iter  40 value 93.295629
iter  50 value 88.479244
iter  60 value 88.141208
iter  70 value 87.910132
final  value 87.897956 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.689878 
iter  10 value 93.590796
iter  20 value 93.565089
iter  30 value 85.637124
iter  40 value 85.390489
iter  50 value 84.107041
iter  60 value 80.637132
iter  70 value 80.341364
final  value 80.340504 
converged
Fitting Repeat 4 

# weights:  507
initial  value 123.134202 
iter  10 value 93.590926
iter  20 value 93.584250
iter  30 value 93.242264
iter  40 value 91.958622
iter  50 value 89.400944
iter  60 value 86.598624
iter  70 value 86.372002
iter  80 value 86.351895
iter  90 value 86.351697
iter 100 value 86.350326
final  value 86.350326 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 117.680316 
iter  10 value 94.060352
iter  20 value 85.580118
final  value 82.988567 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.229820 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.774142 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.274868 
iter  10 value 94.035322
iter  20 value 93.601603
final  value 93.601516 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.947519 
iter  10 value 86.142138
iter  20 value 85.016326
iter  30 value 84.989398
final  value 84.989362 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.055216 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 124.395226 
final  value 93.991525 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.479789 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 119.821949 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.463151 
iter  10 value 93.949469
iter  20 value 93.850031
final  value 93.849361 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.428108 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.153055 
iter  10 value 93.543003
iter  20 value 91.887487
iter  30 value 91.475707
iter  40 value 91.474079
final  value 91.474034 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.760093 
iter  10 value 91.824177
iter  10 value 91.824176
iter  10 value 91.824176
final  value 91.824176 
converged
Fitting Repeat 3 

# weights:  507
initial  value 117.952882 
iter  10 value 92.600394
final  value 92.597507 
converged
Fitting Repeat 4 

# weights:  507
initial  value 100.792366 
iter  10 value 91.824217
final  value 91.824176 
converged
Fitting Repeat 5 

# weights:  507
initial  value 94.359094 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 111.356883 
iter  10 value 93.883095
iter  20 value 81.615859
iter  30 value 79.882217
iter  40 value 79.860036
iter  50 value 79.826720
iter  60 value 79.590796
iter  70 value 79.576175
iter  80 value 79.532155
iter  90 value 79.479804
final  value 79.479774 
converged
Fitting Repeat 2 

# weights:  103
initial  value 100.090224 
iter  10 value 94.050659
iter  20 value 93.554141
iter  30 value 84.278935
iter  40 value 82.705431
iter  50 value 81.408294
iter  60 value 79.039290
iter  70 value 77.978847
iter  80 value 77.814732
iter  90 value 77.597306
iter 100 value 77.510377
final  value 77.510377 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.611057 
iter  10 value 94.076516
iter  20 value 94.058240
iter  30 value 91.243012
iter  40 value 88.932582
iter  50 value 86.455291
iter  60 value 80.055094
iter  70 value 79.579552
iter  80 value 79.576037
iter  90 value 79.510852
iter 100 value 79.480728
final  value 79.480728 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 99.322094 
iter  10 value 94.107304
iter  20 value 93.483210
iter  30 value 92.773682
iter  40 value 92.382697
iter  50 value 80.723793
iter  60 value 79.873022
iter  70 value 79.582937
iter  80 value 79.576848
iter  90 value 79.529100
iter 100 value 79.485520
final  value 79.485520 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 99.268949 
iter  10 value 84.685451
iter  20 value 84.427236
iter  30 value 84.403017
iter  30 value 84.403016
iter  30 value 84.403016
final  value 84.403016 
converged
Fitting Repeat 1 

# weights:  305
initial  value 116.394898 
iter  10 value 94.378798
iter  20 value 82.569741
iter  30 value 80.646515
iter  40 value 79.697417
iter  50 value 78.510776
iter  60 value 77.620682
iter  70 value 77.161451
iter  80 value 77.053192
iter  90 value 75.426349
iter 100 value 75.053758
final  value 75.053758 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.081676 
iter  10 value 94.270252
iter  20 value 91.515130
iter  30 value 79.210057
iter  40 value 79.031927
iter  50 value 78.983826
iter  60 value 78.654515
iter  70 value 77.730264
iter  80 value 76.922254
iter  90 value 76.817308
iter 100 value 76.798794
final  value 76.798794 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.922187 
iter  10 value 93.890008
iter  20 value 84.926462
iter  30 value 83.395193
iter  40 value 79.293023
iter  50 value 78.373884
iter  60 value 78.064975
iter  70 value 77.853894
iter  80 value 77.717309
iter  90 value 77.610735
iter 100 value 77.321939
final  value 77.321939 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 117.878151 
iter  10 value 94.085248
iter  20 value 91.163708
iter  30 value 84.716941
iter  40 value 82.726271
iter  50 value 81.298982
iter  60 value 78.994778
iter  70 value 77.934389
iter  80 value 77.558186
iter  90 value 77.216783
iter 100 value 76.227870
final  value 76.227870 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 100.126106 
iter  10 value 93.947454
iter  20 value 90.490152
iter  30 value 88.006165
iter  40 value 87.066263
iter  50 value 85.970336
iter  60 value 80.393812
iter  70 value 77.803952
iter  80 value 77.568993
iter  90 value 77.449785
iter 100 value 77.322894
final  value 77.322894 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 117.761754 
iter  10 value 95.104984
iter  20 value 91.072733
iter  30 value 86.384703
iter  40 value 80.827649
iter  50 value 78.561783
iter  60 value 78.473024
iter  70 value 78.362440
iter  80 value 78.290258
iter  90 value 78.247291
iter 100 value 78.177545
final  value 78.177545 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 115.735479 
iter  10 value 95.889004
iter  20 value 91.345839
iter  30 value 83.984299
iter  40 value 83.749673
iter  50 value 83.602907
iter  60 value 83.316038
iter  70 value 79.956708
iter  80 value 78.602456
iter  90 value 77.963655
iter 100 value 76.882536
final  value 76.882536 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.551819 
iter  10 value 93.698819
iter  20 value 86.855804
iter  30 value 80.364746
iter  40 value 78.660871
iter  50 value 76.967092
iter  60 value 75.780060
iter  70 value 75.593213
iter  80 value 75.563230
iter  90 value 75.511381
iter 100 value 75.370649
final  value 75.370649 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.749915 
iter  10 value 94.061703
iter  20 value 91.130679
iter  30 value 82.720718
iter  40 value 78.854315
iter  50 value 77.624107
iter  60 value 77.320009
iter  70 value 77.189676
iter  80 value 77.155317
iter  90 value 77.090756
iter 100 value 76.909985
final  value 76.909985 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 117.364742 
iter  10 value 95.294640
iter  20 value 92.933645
iter  30 value 84.348532
iter  40 value 84.128005
iter  50 value 79.678561
iter  60 value 79.367589
iter  70 value 79.163921
iter  80 value 79.066441
iter  90 value 78.347150
iter 100 value 77.315668
final  value 77.315668 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.686123 
final  value 94.054672 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.922744 
final  value 94.054467 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.267772 
final  value 94.054358 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.456551 
final  value 94.054674 
converged
Fitting Repeat 5 

# weights:  103
initial  value 109.072867 
iter  10 value 91.827890
iter  20 value 91.827130
iter  30 value 91.189390
iter  40 value 87.640669
iter  50 value 80.621814
iter  60 value 80.529885
iter  70 value 80.524820
iter  80 value 80.524176
iter  90 value 78.133613
iter 100 value 77.597798
final  value 77.597798 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 101.905419 
iter  10 value 94.058009
iter  20 value 93.863427
iter  30 value 85.162507
iter  40 value 80.352570
iter  50 value 78.779799
iter  60 value 78.775605
iter  70 value 78.691578
iter  80 value 78.244554
iter  90 value 78.229911
iter 100 value 78.029405
final  value 78.029405 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 102.350034 
iter  10 value 91.830040
iter  20 value 91.827326
iter  30 value 82.878075
final  value 82.869593 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.560060 
iter  10 value 94.058148
iter  20 value 94.016017
iter  30 value 93.327878
iter  40 value 93.287921
iter  50 value 93.286118
final  value 93.285950 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.202890 
iter  10 value 94.055460
iter  20 value 93.962988
iter  30 value 77.744871
iter  40 value 77.634608
iter  50 value 77.633563
iter  60 value 77.633221
iter  70 value 77.534248
iter  80 value 75.408101
iter  90 value 73.736380
iter 100 value 73.276338
final  value 73.276338 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 92.774579 
iter  10 value 84.482802
iter  20 value 82.739201
iter  30 value 82.734761
iter  40 value 82.718307
iter  50 value 82.666395
iter  60 value 82.664062
iter  70 value 82.660683
iter  80 value 82.660501
final  value 82.660486 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.544236 
iter  10 value 87.313300
iter  20 value 79.629500
iter  30 value 76.908580
iter  40 value 76.484602
iter  50 value 76.467515
iter  50 value 76.467514
iter  60 value 76.404936
iter  70 value 76.109257
iter  80 value 76.084710
iter  90 value 76.083706
iter 100 value 74.419111
final  value 74.419111 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 108.963140 
iter  10 value 94.047022
iter  20 value 92.610662
iter  30 value 87.538462
iter  40 value 87.511214
iter  50 value 87.510932
iter  60 value 87.508934
iter  70 value 87.508309
iter  70 value 87.508308
iter  70 value 87.508308
final  value 87.508308 
converged
Fitting Repeat 3 

# weights:  507
initial  value 111.827511 
iter  10 value 94.046384
iter  20 value 93.909574
iter  30 value 85.611942
iter  40 value 85.608460
iter  50 value 85.586246
iter  60 value 81.010428
iter  70 value 80.150376
iter  80 value 75.991417
iter  90 value 74.559796
iter 100 value 74.298301
final  value 74.298301 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 98.764058 
iter  10 value 94.046474
iter  20 value 94.033515
iter  30 value 80.905720
iter  40 value 79.986589
iter  50 value 76.863913
iter  60 value 76.461847
iter  70 value 76.387407
final  value 76.386377 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.219861 
iter  10 value 94.046981
iter  20 value 94.039683
iter  30 value 84.552579
iter  40 value 77.580468
iter  50 value 77.551959
iter  60 value 77.544677
iter  70 value 77.542554
iter  80 value 77.541541
final  value 77.541525 
converged
Fitting Repeat 1 

# weights:  507
initial  value 133.224763 
iter  10 value 117.529303
iter  20 value 114.941724
iter  30 value 108.853767
iter  40 value 104.073696
iter  50 value 102.118678
iter  60 value 101.870560
iter  70 value 101.280164
iter  80 value 100.891489
iter  90 value 100.874543
iter 100 value 100.820798
final  value 100.820798 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 152.552900 
iter  10 value 117.955558
iter  20 value 109.786141
iter  30 value 106.332947
iter  40 value 105.448834
iter  50 value 102.386736
iter  60 value 102.090944
iter  70 value 101.914364
iter  80 value 101.744772
iter  90 value 101.510146
iter 100 value 101.012682
final  value 101.012682 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 131.118077 
iter  10 value 119.385967
iter  20 value 117.505494
iter  30 value 115.134321
iter  40 value 107.086890
iter  50 value 105.592315
iter  60 value 105.212344
iter  70 value 104.005693
iter  80 value 103.175473
iter  90 value 102.501583
iter 100 value 101.658040
final  value 101.658040 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 133.298412 
iter  10 value 118.069499
iter  20 value 117.813230
iter  30 value 115.661407
iter  40 value 113.828804
iter  50 value 110.849774
iter  60 value 110.591661
iter  70 value 108.941623
iter  80 value 106.938959
iter  90 value 104.700353
iter 100 value 103.127604
final  value 103.127604 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 135.303825 
iter  10 value 117.982820
iter  20 value 115.864713
iter  30 value 115.394683
iter  40 value 111.203219
iter  50 value 108.058627
iter  60 value 106.203347
iter  70 value 103.256510
iter  80 value 103.145275
iter  90 value 103.072977
iter 100 value 102.806371
final  value 102.806371 
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases


RUNIT TEST PROTOCOL -- Wed Sep 10 20:53:18 2025 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 18.574   0.412  78.518