##############################################################################
##############################################################################
###
### 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_1.17.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2026-01-15 r89304)
* 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 Sonoma 14.8.3
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.17.2’
* 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
FSmethod      19.200  0.922  20.868
corr_plot     19.056  0.865  20.345
var_imp       18.650  1.004  20.544
pred_ensembel  6.496  0.115   6.107
enrichfindP    0.206  0.037  12.042
* 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-3.23-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/4.6-arm64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.17.2’
** 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 Under development (unstable) (2026-01-15 r89304) -- "Unsuffered Consequences"
Copyright (C) 2026 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 103.772386 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.670196 
final  value 93.528329 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.556778 
iter  10 value 87.273814
iter  20 value 86.099775
iter  30 value 86.085408
iter  40 value 86.039451
final  value 86.009524 
converged
Fitting Repeat 4 

# weights:  103
initial  value 112.614186 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.080179 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.323128 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 110.029163 
final  value 93.582418 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.499306 
final  value 93.582418 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.616452 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.170151 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.512732 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 102.599646 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 95.423588 
iter  10 value 93.582418
iter  10 value 93.582418
iter  10 value 93.582418
final  value 93.582418 
converged
Fitting Repeat 4 

# weights:  507
initial  value 115.034417 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.717410 
final  value 93.582418 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.843513 
iter  10 value 93.961719
iter  20 value 93.656004
iter  30 value 90.594615
iter  40 value 87.170283
iter  50 value 86.992661
iter  60 value 86.479034
iter  70 value 86.120903
iter  80 value 86.094618
final  value 86.093863 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.367839 
iter  10 value 94.042109
iter  20 value 89.878075
iter  30 value 87.890287
iter  40 value 86.335027
iter  50 value 84.906445
iter  60 value 84.751539
iter  70 value 84.750449
iter  70 value 84.750449
iter  70 value 84.750449
final  value 84.750449 
converged
Fitting Repeat 3 

# weights:  103
initial  value 107.377787 
iter  10 value 93.595665
iter  20 value 87.945513
iter  30 value 87.401403
iter  40 value 87.222652
iter  50 value 87.072812
iter  60 value 86.999221
final  value 86.997653 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.382550 
iter  10 value 94.081929
iter  20 value 94.001437
iter  30 value 92.802242
iter  40 value 87.706274
iter  50 value 87.278510
iter  60 value 86.505155
iter  70 value 86.323541
iter  80 value 86.317697
iter  90 value 86.315335
final  value 86.315316 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.258274 
iter  10 value 94.047673
iter  20 value 88.713252
iter  30 value 85.748686
iter  40 value 85.367236
iter  50 value 85.034998
iter  60 value 84.709899
iter  70 value 84.707614
final  value 84.707609 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.363294 
iter  10 value 93.854457
iter  20 value 90.835898
iter  30 value 89.499210
iter  40 value 87.787680
iter  50 value 87.127368
iter  60 value 85.151057
iter  70 value 84.635597
iter  80 value 84.445430
iter  90 value 84.299236
iter 100 value 84.178864
final  value 84.178864 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.732830 
iter  10 value 93.676979
iter  20 value 89.704576
iter  30 value 86.808206
iter  40 value 85.752477
iter  50 value 85.460893
iter  60 value 85.280413
iter  70 value 85.020171
iter  80 value 84.843572
iter  90 value 84.806374
iter 100 value 84.616711
final  value 84.616711 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.592886 
iter  10 value 94.025525
iter  20 value 93.698104
iter  30 value 93.433136
iter  40 value 88.750297
iter  50 value 86.737315
iter  60 value 86.459164
iter  70 value 85.411798
iter  80 value 84.972035
iter  90 value 84.768429
iter 100 value 84.634210
final  value 84.634210 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.644622 
iter  10 value 93.953952
iter  20 value 91.398802
iter  30 value 90.893093
iter  40 value 89.290637
iter  50 value 85.327456
iter  60 value 84.727021
iter  70 value 84.531827
iter  80 value 84.487911
iter  90 value 84.390690
iter 100 value 84.006765
final  value 84.006765 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 99.824374 
iter  10 value 93.018485
iter  20 value 91.378495
iter  30 value 90.539224
iter  40 value 86.001943
iter  50 value 85.817335
iter  60 value 85.598416
iter  70 value 85.165674
iter  80 value 84.510323
iter  90 value 84.066035
iter 100 value 83.959213
final  value 83.959213 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 109.137722 
iter  10 value 93.761966
iter  20 value 92.914110
iter  30 value 91.280426
iter  40 value 87.364576
iter  50 value 86.910507
iter  60 value 85.969024
iter  70 value 84.813708
iter  80 value 84.512851
iter  90 value 84.193184
iter 100 value 83.755920
final  value 83.755920 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.300217 
iter  10 value 94.055573
iter  20 value 92.602107
iter  30 value 91.567350
iter  40 value 89.491800
iter  50 value 88.842826
iter  60 value 88.374758
iter  70 value 87.180798
iter  80 value 85.500168
iter  90 value 84.452915
iter 100 value 84.214274
final  value 84.214274 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 144.906744 
iter  10 value 95.556356
iter  20 value 88.822864
iter  30 value 87.364351
iter  40 value 86.289286
iter  50 value 85.523048
iter  60 value 84.950839
iter  70 value 84.681640
iter  80 value 83.910280
iter  90 value 83.816982
iter 100 value 83.788146
final  value 83.788146 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 130.916399 
iter  10 value 93.854712
iter  20 value 93.604538
iter  30 value 92.756098
iter  40 value 91.989764
iter  50 value 91.784613
iter  60 value 89.731556
iter  70 value 88.738156
iter  80 value 87.594978
iter  90 value 86.967876
iter 100 value 86.094710
final  value 86.094710 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 126.437601 
iter  10 value 94.904677
iter  20 value 90.047889
iter  30 value 88.265203
iter  40 value 86.510924
iter  50 value 85.007903
iter  60 value 83.965462
iter  70 value 83.889153
iter  80 value 83.861678
iter  90 value 83.817934
iter 100 value 83.758186
final  value 83.758186 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 110.140113 
final  value 94.055172 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.435819 
final  value 94.054762 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.376237 
iter  10 value 94.054406
final  value 94.052914 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.300390 
iter  10 value 94.054726
iter  20 value 94.054282
final  value 94.052922 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.445058 
final  value 94.054467 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.219198 
iter  10 value 94.057914
iter  20 value 93.879368
iter  30 value 93.583338
iter  40 value 93.582888
iter  50 value 93.582728
iter  60 value 92.708810
iter  70 value 88.413349
iter  80 value 86.735719
iter  90 value 86.624495
iter 100 value 86.358328
final  value 86.358328 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.651981 
iter  10 value 91.169702
iter  20 value 86.734513
iter  30 value 86.711704
iter  40 value 86.691618
iter  50 value 86.690525
iter  60 value 86.463593
iter  70 value 86.282665
iter  80 value 86.269880
final  value 86.269147 
converged
Fitting Repeat 3 

# weights:  305
initial  value 98.401506 
iter  10 value 93.294370
iter  20 value 87.981347
iter  30 value 86.544183
iter  40 value 86.539330
iter  50 value 86.539217
iter  60 value 86.539102
final  value 86.538951 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.186748 
iter  10 value 93.587274
iter  20 value 93.583241
final  value 93.582646 
converged
Fitting Repeat 5 

# weights:  305
initial  value 96.392984 
iter  10 value 94.057761
iter  20 value 94.052893
iter  30 value 88.625835
iter  40 value 85.030821
iter  50 value 84.639393
iter  60 value 83.830015
iter  70 value 82.593360
iter  80 value 82.524737
iter  90 value 82.489163
final  value 82.487419 
converged
Fitting Repeat 1 

# weights:  507
initial  value 100.448981 
iter  10 value 93.590757
iter  20 value 93.584354
iter  30 value 93.558314
iter  40 value 92.388477
iter  50 value 88.350403
iter  60 value 85.161045
iter  70 value 84.917415
iter  80 value 84.913978
final  value 84.913898 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.042548 
iter  10 value 93.725022
iter  20 value 93.587062
final  value 93.583956 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.912272 
iter  10 value 93.591392
iter  20 value 93.583462
final  value 93.582888 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.454720 
iter  10 value 94.061179
iter  20 value 93.916045
iter  30 value 90.485015
iter  40 value 87.627526
iter  50 value 87.296582
iter  60 value 87.181911
iter  70 value 86.721784
iter  80 value 86.662467
final  value 86.661907 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.687356 
iter  10 value 93.324542
iter  20 value 92.711267
iter  30 value 92.545950
iter  40 value 92.542852
iter  50 value 91.948290
iter  60 value 90.646622
iter  70 value 90.523029
iter  80 value 90.360065
final  value 90.278096 
converged
Fitting Repeat 1 

# weights:  103
initial  value 115.507279 
final  value 93.701657 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.715375 
iter  10 value 85.758750
iter  20 value 84.542634
iter  30 value 84.533380
final  value 84.533338 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.925389 
iter  10 value 93.773247
final  value 93.772973 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.301388 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.328552 
iter  10 value 93.772983
final  value 93.772973 
converged
Fitting Repeat 1 

# weights:  305
initial  value 121.925158 
final  value 93.022222 
converged
Fitting Repeat 2 

# weights:  305
initial  value 94.680554 
iter  10 value 92.906246
final  value 92.904867 
converged
Fitting Repeat 3 

# weights:  305
initial  value 109.655460 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 113.552373 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.838159 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.588974 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 91.920574 
iter  10 value 84.822859
final  value 84.530200 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.575415 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.539834 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.799661 
final  value 93.772973 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.552539 
iter  10 value 94.477773
iter  20 value 86.254920
iter  30 value 85.001910
iter  40 value 82.779517
iter  50 value 82.178244
iter  60 value 81.707530
iter  70 value 81.352832
final  value 81.352014 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.078696 
iter  10 value 94.699041
iter  20 value 94.416404
iter  30 value 87.679787
iter  40 value 86.347320
iter  50 value 86.181595
iter  60 value 83.430290
iter  70 value 81.589114
iter  80 value 81.180494
iter  90 value 80.933218
final  value 80.928572 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.152584 
iter  10 value 94.129742
iter  20 value 84.733059
iter  30 value 82.539943
iter  40 value 81.792805
iter  50 value 81.679845
iter  60 value 81.321601
iter  70 value 81.028778
final  value 81.028614 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.375689 
iter  10 value 94.480247
iter  20 value 93.627396
iter  30 value 93.395173
iter  40 value 93.173580
iter  50 value 86.299180
iter  60 value 83.373805
iter  70 value 80.758212
iter  80 value 80.472257
iter  90 value 80.451464
iter 100 value 80.450451
final  value 80.450451 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 103.183224 
iter  10 value 94.130283
iter  20 value 93.316670
iter  30 value 89.642039
iter  40 value 83.967572
iter  50 value 83.318957
iter  60 value 82.706323
iter  70 value 81.476510
iter  80 value 81.449091
final  value 81.446051 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.158442 
iter  10 value 93.875646
iter  20 value 90.518263
iter  30 value 83.378234
iter  40 value 82.500469
iter  50 value 82.248057
iter  60 value 80.807349
iter  70 value 80.437829
iter  80 value 80.138690
iter  90 value 79.420969
iter 100 value 77.793720
final  value 77.793720 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 125.724655 
iter  10 value 94.487543
iter  20 value 88.159923
iter  30 value 85.844000
iter  40 value 82.195921
iter  50 value 81.732682
iter  60 value 80.775926
iter  70 value 80.174958
iter  80 value 78.089292
iter  90 value 77.879553
iter 100 value 77.633174
final  value 77.633174 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.533706 
iter  10 value 94.417955
iter  20 value 93.268367
iter  30 value 93.175822
iter  40 value 88.810362
iter  50 value 85.752890
iter  60 value 83.387097
iter  70 value 81.965320
iter  80 value 81.616043
iter  90 value 81.146858
iter 100 value 80.961027
final  value 80.961027 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 125.725355 
iter  10 value 95.043171
iter  20 value 83.723509
iter  30 value 82.273941
iter  40 value 81.153852
iter  50 value 81.037939
iter  60 value 80.699069
iter  70 value 80.332045
iter  80 value 80.282904
iter  90 value 80.254163
iter 100 value 79.181732
final  value 79.181732 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.163535 
iter  10 value 94.178344
iter  20 value 92.771646
iter  30 value 83.487982
iter  40 value 82.281388
iter  50 value 79.886182
iter  60 value 78.046840
iter  70 value 77.607157
iter  80 value 77.169723
iter  90 value 76.682788
iter 100 value 76.474781
final  value 76.474781 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 108.182791 
iter  10 value 94.012238
iter  20 value 86.258573
iter  30 value 85.642197
iter  40 value 84.082229
iter  50 value 81.027436
iter  60 value 79.317556
iter  70 value 78.502228
iter  80 value 77.224095
iter  90 value 76.881426
iter 100 value 76.541949
final  value 76.541949 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 113.394043 
iter  10 value 94.356063
iter  20 value 91.487705
iter  30 value 86.267790
iter  40 value 84.206331
iter  50 value 79.818450
iter  60 value 79.452132
iter  70 value 78.976121
iter  80 value 78.542457
iter  90 value 78.400651
iter 100 value 78.296666
final  value 78.296666 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.409323 
iter  10 value 94.084088
iter  20 value 93.254270
iter  30 value 93.194940
iter  40 value 93.175612
iter  50 value 86.302419
iter  60 value 78.552350
iter  70 value 78.318546
iter  80 value 77.587494
iter  90 value 76.907208
iter 100 value 76.564819
final  value 76.564819 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 120.562342 
iter  10 value 90.216295
iter  20 value 85.160436
iter  30 value 82.487196
iter  40 value 80.515822
iter  50 value 77.799871
iter  60 value 77.076263
iter  70 value 76.724730
iter  80 value 76.546481
iter  90 value 76.289155
iter 100 value 76.176918
final  value 76.176918 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.184818 
iter  10 value 93.258169
iter  20 value 86.209061
iter  30 value 83.869555
iter  40 value 81.722840
iter  50 value 80.803595
iter  60 value 79.206944
iter  70 value 77.777472
iter  80 value 77.226364
iter  90 value 77.059844
iter 100 value 77.051232
final  value 77.051232 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.276548 
iter  10 value 94.485852
final  value 94.484216 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.702289 
iter  10 value 94.266080
iter  20 value 93.769664
iter  30 value 93.769167
iter  40 value 93.769067
iter  50 value 93.768691
iter  60 value 93.650250
iter  70 value 93.010894
iter  80 value 93.010477
final  value 93.010440 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.982242 
final  value 94.485842 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.257441 
final  value 94.485999 
converged
Fitting Repeat 5 

# weights:  103
initial  value 103.330787 
final  value 94.485963 
converged
Fitting Repeat 1 

# weights:  305
initial  value 126.393778 
iter  10 value 93.778828
iter  20 value 93.777210
iter  30 value 93.122096
iter  40 value 89.825746
iter  50 value 84.811387
final  value 84.803654 
converged
Fitting Repeat 2 

# weights:  305
initial  value 109.219829 
iter  10 value 94.489027
iter  20 value 94.484269
iter  30 value 90.001649
iter  40 value 86.518365
iter  50 value 81.992133
iter  60 value 80.584797
iter  70 value 79.850974
iter  80 value 79.240393
iter  90 value 79.176441
iter 100 value 79.172626
final  value 79.172626 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.241114 
iter  10 value 93.778235
iter  20 value 93.774914
iter  30 value 93.702033
iter  40 value 93.699694
final  value 93.699632 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.971462 
iter  10 value 94.489117
iter  20 value 94.468602
iter  30 value 93.038123
final  value 93.022748 
converged
Fitting Repeat 5 

# weights:  305
initial  value 113.522304 
iter  10 value 94.489557
iter  20 value 94.472163
iter  30 value 86.890983
iter  40 value 83.740176
iter  50 value 83.287147
iter  60 value 83.154557
iter  70 value 83.096998
iter  80 value 83.025282
iter  90 value 83.025160
final  value 83.024543 
converged
Fitting Repeat 1 

# weights:  507
initial  value 108.431367 
iter  10 value 94.492399
iter  20 value 94.484595
iter  30 value 93.313345
iter  40 value 81.434706
iter  50 value 81.140259
iter  60 value 80.008704
iter  70 value 76.170060
iter  80 value 74.738326
iter  90 value 74.637543
iter 100 value 74.635662
final  value 74.635662 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 104.216702 
iter  10 value 94.491655
iter  20 value 93.758570
iter  30 value 90.450128
iter  40 value 83.518981
iter  50 value 80.904410
iter  60 value 80.903070
iter  70 value 80.902166
final  value 80.901927 
converged
Fitting Repeat 3 

# weights:  507
initial  value 118.081505 
iter  10 value 94.492057
iter  20 value 94.266039
final  value 93.702132 
converged
Fitting Repeat 4 

# weights:  507
initial  value 95.264223 
iter  10 value 93.781769
iter  20 value 93.772910
iter  30 value 93.251170
iter  40 value 92.944341
iter  50 value 92.926632
final  value 92.926540 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.585119 
iter  10 value 94.571628
iter  20 value 94.557568
iter  30 value 84.598730
iter  40 value 83.605542
iter  50 value 80.780030
iter  60 value 80.644683
iter  70 value 79.657331
iter  80 value 79.622456
iter  90 value 79.615827
iter 100 value 79.515780
final  value 79.515780 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.268205 
final  value 94.479532 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.556798 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.903461 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.422178 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.465505 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.037269 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.549103 
final  value 94.467391 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.685270 
final  value 94.484138 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.879914 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 117.368323 
iter  10 value 93.506851
iter  20 value 92.979995
iter  30 value 92.638754
iter  40 value 92.630687
iter  40 value 92.630686
iter  40 value 92.630686
final  value 92.630686 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.437013 
iter  10 value 91.387044
iter  20 value 90.857338
iter  30 value 90.844440
iter  40 value 90.832323
final  value 90.832214 
converged
Fitting Repeat 2 

# weights:  507
initial  value 106.405835 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 107.402177 
iter  10 value 94.090627
final  value 94.090583 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.263162 
iter  10 value 87.837334
final  value 87.316697 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.387197 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.862676 
iter  10 value 94.449447
iter  20 value 91.084547
iter  30 value 87.246428
iter  40 value 87.055618
iter  50 value 83.223632
iter  60 value 81.284074
iter  70 value 80.864899
iter  80 value 80.551201
iter  90 value 80.216838
iter 100 value 79.945004
final  value 79.945004 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 102.000070 
iter  10 value 94.475534
iter  20 value 93.136829
iter  30 value 92.769439
iter  40 value 89.532050
iter  50 value 89.417199
iter  60 value 85.339053
iter  70 value 82.907093
iter  80 value 81.728788
iter  90 value 80.836237
iter 100 value 80.611245
final  value 80.611245 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 103.311973 
iter  10 value 94.458691
iter  20 value 94.190760
iter  30 value 94.085667
iter  40 value 94.072910
iter  50 value 89.634518
iter  60 value 89.060638
iter  70 value 88.551429
iter  80 value 83.256168
iter  90 value 82.496377
iter 100 value 80.913452
final  value 80.913452 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 96.252378 
iter  10 value 94.417241
iter  20 value 92.292044
iter  30 value 92.094536
iter  40 value 88.817697
iter  50 value 83.543873
iter  60 value 82.701629
iter  70 value 82.554333
iter  80 value 81.683958
iter  90 value 81.145747
iter 100 value 80.987647
final  value 80.987647 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 97.943571 
iter  10 value 93.982664
iter  20 value 85.442680
iter  30 value 84.454961
iter  40 value 84.210020
iter  50 value 83.123045
iter  60 value 81.992553
iter  70 value 81.771870
iter  80 value 80.375773
iter  90 value 79.936763
iter 100 value 79.929403
final  value 79.929403 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 129.767781 
iter  10 value 95.193058
iter  20 value 94.570279
iter  30 value 94.521810
iter  40 value 93.883726
iter  50 value 88.820025
iter  60 value 81.489037
iter  70 value 80.236304
iter  80 value 79.004678
iter  90 value 78.859051
iter 100 value 78.797853
final  value 78.797853 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 115.233611 
iter  10 value 94.390044
iter  20 value 92.893952
iter  30 value 92.414977
iter  40 value 91.976106
iter  50 value 85.962960
iter  60 value 83.492693
iter  70 value 83.077798
iter  80 value 82.521926
iter  90 value 81.790268
iter 100 value 81.371406
final  value 81.371406 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.587829 
iter  10 value 94.521667
iter  20 value 86.120469
iter  30 value 83.321239
iter  40 value 79.884989
iter  50 value 78.812742
iter  60 value 78.434819
iter  70 value 78.228729
iter  80 value 78.119696
iter  90 value 78.068093
iter 100 value 77.920422
final  value 77.920422 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 111.655575 
iter  10 value 94.380149
iter  20 value 92.847629
iter  30 value 91.986217
iter  40 value 91.351471
iter  50 value 87.199740
iter  60 value 86.274309
iter  70 value 85.702540
iter  80 value 83.928550
iter  90 value 81.919332
iter 100 value 81.188071
final  value 81.188071 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 109.964299 
iter  10 value 94.744718
iter  20 value 92.703707
iter  30 value 81.868104
iter  40 value 81.512322
iter  50 value 81.239967
iter  60 value 80.279306
iter  70 value 79.327383
iter  80 value 78.600197
iter  90 value 78.555155
iter 100 value 78.374856
final  value 78.374856 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.809922 
iter  10 value 94.502889
iter  20 value 88.093489
iter  30 value 84.438980
iter  40 value 82.183206
iter  50 value 80.424460
iter  60 value 80.025979
iter  70 value 79.132072
iter  80 value 78.865653
iter  90 value 78.559304
iter 100 value 78.302548
final  value 78.302548 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.066818 
iter  10 value 94.691882
iter  20 value 84.955372
iter  30 value 83.282523
iter  40 value 82.906029
iter  50 value 81.684467
iter  60 value 79.993641
iter  70 value 79.529939
iter  80 value 78.810560
iter  90 value 78.679499
iter 100 value 78.576090
final  value 78.576090 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.975965 
iter  10 value 91.882144
iter  20 value 87.007230
iter  30 value 85.013679
iter  40 value 83.780163
iter  50 value 82.321293
iter  60 value 81.978758
iter  70 value 81.698494
iter  80 value 81.523325
iter  90 value 81.371501
iter 100 value 81.224566
final  value 81.224566 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.212090 
iter  10 value 95.935946
iter  20 value 86.244705
iter  30 value 82.467987
iter  40 value 81.053164
iter  50 value 80.555474
iter  60 value 79.914650
iter  70 value 79.195807
iter  80 value 78.269641
iter  90 value 77.865018
iter 100 value 77.634056
final  value 77.634056 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 115.967796 
iter  10 value 94.466023
iter  20 value 94.114736
iter  30 value 84.795639
iter  40 value 82.166986
iter  50 value 80.635281
iter  60 value 80.378928
iter  70 value 80.057778
iter  80 value 79.805988
iter  90 value 79.728787
iter 100 value 79.630478
final  value 79.630478 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.270953 
iter  10 value 94.485704
iter  20 value 94.482769
iter  30 value 88.925627
iter  40 value 83.571576
iter  50 value 83.560802
iter  60 value 83.559293
iter  70 value 82.985354
iter  80 value 82.982619
iter  90 value 82.980370
iter 100 value 82.980290
final  value 82.980290 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.460768 
final  value 94.485811 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.264792 
final  value 94.485885 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.315231 
final  value 94.485881 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.363530 
iter  10 value 94.468451
iter  20 value 94.466872
iter  30 value 94.356029
iter  40 value 92.469526
iter  50 value 91.914313
final  value 91.914292 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.013626 
iter  10 value 94.132680
iter  20 value 93.698039
iter  30 value 93.635891
iter  40 value 93.619532
iter  50 value 93.618115
iter  60 value 93.616136
iter  70 value 91.608826
iter  80 value 82.940652
iter  90 value 81.999899
iter 100 value 81.984069
final  value 81.984069 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.193188 
iter  10 value 94.209551
iter  20 value 94.207261
iter  30 value 87.548703
iter  40 value 86.277571
final  value 86.277290 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.301125 
iter  10 value 94.488943
iter  20 value 94.484209
iter  30 value 87.037385
iter  40 value 86.014514
iter  50 value 85.515703
iter  50 value 85.515703
iter  50 value 85.515703
final  value 85.515703 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.243132 
iter  10 value 94.488104
iter  20 value 94.484231
final  value 94.484221 
converged
Fitting Repeat 5 

# weights:  305
initial  value 111.768889 
iter  10 value 94.489201
iter  20 value 94.410576
iter  30 value 86.943059
iter  40 value 83.475336
iter  50 value 83.474878
iter  60 value 83.334121
iter  70 value 82.872528
iter  80 value 81.703373
iter  90 value 81.675655
iter 100 value 81.675311
final  value 81.675311 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 98.076864 
iter  10 value 94.153740
iter  20 value 92.986159
iter  30 value 92.065798
iter  40 value 86.908037
iter  50 value 82.215468
iter  60 value 82.196902
iter  70 value 79.319603
iter  80 value 78.984322
iter  90 value 78.972145
iter 100 value 78.971282
final  value 78.971282 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 120.999375 
iter  10 value 94.493713
iter  20 value 93.777397
iter  30 value 93.726569
iter  40 value 93.677822
iter  50 value 93.594421
final  value 93.593201 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.079445 
iter  10 value 94.442384
iter  20 value 94.435771
final  value 94.434835 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.469051 
iter  10 value 94.492114
iter  20 value 94.398934
iter  30 value 83.919181
iter  40 value 77.632410
iter  50 value 77.453501
iter  60 value 76.872963
iter  70 value 76.768088
iter  80 value 76.763786
iter  90 value 76.750686
iter 100 value 76.725342
final  value 76.725342 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 96.219133 
iter  10 value 93.852809
iter  20 value 92.370800
iter  30 value 92.095158
final  value 92.093612 
converged
Fitting Repeat 1 

# weights:  103
initial  value 95.441965 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.162715 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 89.114993 
iter  10 value 85.618505
iter  20 value 85.101851
iter  30 value 85.065314
iter  40 value 84.660374
iter  50 value 84.649520
final  value 84.649496 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.383637 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 104.366641 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.189405 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.222847 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 114.464549 
iter  10 value 93.288889
iter  10 value 93.288889
iter  10 value 93.288889
final  value 93.288889 
converged
Fitting Repeat 4 

# weights:  305
initial  value 106.597908 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  305
initial  value 119.187315 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.930414 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.743365 
final  value 93.915746 
converged
Fitting Repeat 3 

# weights:  507
initial  value 125.067525 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.696639 
final  value 93.900821 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.089487 
iter  10 value 93.324101
iter  20 value 93.288903
final  value 93.288889 
converged
Fitting Repeat 1 

# weights:  103
initial  value 104.359676 
iter  10 value 94.056749
iter  20 value 93.817989
iter  30 value 90.269718
iter  40 value 88.985028
iter  50 value 85.328406
iter  60 value 84.892155
iter  70 value 83.974542
iter  80 value 83.401491
iter  90 value 83.282656
final  value 83.282429 
converged
Fitting Repeat 2 

# weights:  103
initial  value 111.044155 
iter  10 value 94.081965
iter  20 value 94.056762
iter  30 value 92.566032
iter  40 value 87.138929
iter  50 value 85.704937
iter  60 value 85.466589
iter  70 value 84.999866
iter  80 value 84.598550
iter  90 value 84.444852
iter 100 value 84.342761
final  value 84.342761 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.983962 
iter  10 value 94.072824
iter  20 value 94.032023
iter  30 value 92.912743
iter  40 value 89.585885
iter  50 value 88.115621
iter  60 value 86.990100
iter  70 value 84.778762
iter  80 value 84.545899
iter  90 value 84.462862
iter 100 value 83.512613
final  value 83.512613 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 95.983469 
iter  10 value 93.652776
iter  20 value 86.644111
iter  30 value 86.196386
iter  40 value 86.020661
iter  50 value 85.886860
iter  60 value 85.825289
final  value 85.817533 
converged
Fitting Repeat 5 

# weights:  103
initial  value 100.092750 
iter  10 value 94.044606
iter  20 value 93.901844
iter  30 value 90.868308
iter  40 value 85.721967
iter  50 value 85.525759
iter  60 value 85.405755
iter  70 value 85.098100
iter  80 value 85.030998
iter  90 value 83.569817
iter 100 value 83.538198
final  value 83.538198 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 119.141843 
iter  10 value 93.888400
iter  20 value 85.831958
iter  30 value 84.074123
iter  40 value 83.001768
iter  50 value 82.585186
iter  60 value 82.325247
iter  70 value 82.176652
iter  80 value 82.137809
iter  90 value 82.121283
iter 100 value 82.039469
final  value 82.039469 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 136.355948 
iter  10 value 94.232219
iter  20 value 93.452971
iter  30 value 91.554860
iter  40 value 86.890116
iter  50 value 85.574235
iter  60 value 84.777784
iter  70 value 84.692676
iter  80 value 83.733454
iter  90 value 83.278296
iter 100 value 83.214088
final  value 83.214088 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.822806 
iter  10 value 94.138908
iter  20 value 94.004283
iter  30 value 93.555548
iter  40 value 88.786520
iter  50 value 86.412162
iter  60 value 86.030055
iter  70 value 84.954507
iter  80 value 84.681649
iter  90 value 84.540824
iter 100 value 84.438495
final  value 84.438495 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.023998 
iter  10 value 93.774416
iter  20 value 87.755959
iter  30 value 85.691715
iter  40 value 84.426747
iter  50 value 83.026926
iter  60 value 82.643078
iter  70 value 82.398184
iter  80 value 82.099303
iter  90 value 81.937730
iter 100 value 81.873202
final  value 81.873202 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 118.211731 
iter  10 value 94.065378
iter  20 value 92.253622
iter  30 value 86.201125
iter  40 value 85.038530
iter  50 value 84.522467
iter  60 value 84.162822
iter  70 value 83.943883
iter  80 value 83.704406
iter  90 value 83.632538
iter 100 value 83.542307
final  value 83.542307 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 126.871111 
iter  10 value 93.416979
iter  20 value 86.082755
iter  30 value 85.726914
iter  40 value 84.513919
iter  50 value 83.557435
iter  60 value 83.030977
iter  70 value 82.741306
iter  80 value 82.587986
iter  90 value 82.556243
iter 100 value 82.517304
final  value 82.517304 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 110.294333 
iter  10 value 94.733847
iter  20 value 91.027388
iter  30 value 87.180122
iter  40 value 86.369971
iter  50 value 85.650576
iter  60 value 84.370609
iter  70 value 83.186092
iter  80 value 83.030981
iter  90 value 82.543962
iter 100 value 82.332431
final  value 82.332431 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 123.287876 
iter  10 value 93.725368
iter  20 value 88.227139
iter  30 value 85.626113
iter  40 value 85.437282
iter  50 value 84.757142
iter  60 value 83.381815
iter  70 value 82.651060
iter  80 value 82.245555
iter  90 value 81.929269
iter 100 value 81.830905
final  value 81.830905 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 106.263780 
iter  10 value 93.857302
iter  20 value 86.855933
iter  30 value 84.508486
iter  40 value 83.272865
iter  50 value 83.009976
iter  60 value 82.836202
iter  70 value 82.606991
iter  80 value 82.365799
iter  90 value 82.299828
iter 100 value 82.150713
final  value 82.150713 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.106307 
iter  10 value 93.616346
iter  20 value 85.857140
iter  30 value 85.479533
iter  40 value 84.891783
iter  50 value 84.518258
iter  60 value 83.634819
iter  70 value 82.882009
iter  80 value 82.461830
iter  90 value 82.328295
iter 100 value 82.213205
final  value 82.213205 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.644342 
final  value 94.054396 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.392544 
final  value 94.054527 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.420748 
final  value 94.054920 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.080620 
final  value 94.054581 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.618573 
final  value 94.054632 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.272194 
iter  10 value 94.057662
iter  20 value 92.829332
iter  30 value 92.768751
final  value 92.768720 
converged
Fitting Repeat 2 

# weights:  305
initial  value 99.248601 
iter  10 value 94.037405
iter  20 value 94.034808
iter  30 value 94.032050
iter  40 value 94.022726
iter  50 value 91.919978
final  value 91.919951 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.046561 
iter  10 value 93.296239
iter  20 value 93.291447
iter  30 value 91.042076
iter  40 value 91.005218
final  value 91.005128 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.867256 
iter  10 value 94.058323
iter  20 value 93.922768
iter  30 value 93.916096
iter  40 value 93.915980
iter  50 value 93.915942
iter  60 value 93.915865
final  value 93.915859 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.219678 
iter  10 value 94.057733
iter  20 value 93.985393
iter  30 value 86.477949
iter  40 value 85.049624
iter  50 value 82.520665
iter  60 value 81.977506
iter  70 value 81.949142
iter  80 value 81.945198
iter  90 value 81.944797
iter 100 value 81.944262
final  value 81.944262 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 100.837655 
iter  10 value 94.061139
iter  20 value 93.184982
iter  30 value 86.319967
iter  40 value 85.230146
iter  50 value 85.179186
iter  60 value 85.027541
iter  70 value 84.837539
iter  80 value 84.627732
iter  90 value 81.858856
iter 100 value 81.753249
final  value 81.753249 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 111.726221 
iter  10 value 92.841190
iter  20 value 92.820978
iter  30 value 88.790997
iter  40 value 88.696767
iter  50 value 88.149620
iter  60 value 87.947395
iter  70 value 87.943857
iter  80 value 84.712963
iter  90 value 84.365184
iter 100 value 84.355132
final  value 84.355132 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 103.341685 
iter  10 value 93.874567
iter  20 value 93.869099
iter  30 value 93.867064
iter  40 value 93.866292
final  value 93.866244 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.895849 
iter  10 value 94.060769
iter  20 value 93.831939
iter  30 value 85.180849
iter  40 value 85.024200
iter  50 value 85.017907
final  value 85.017816 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.240138 
iter  10 value 94.059809
iter  20 value 93.497220
iter  30 value 87.445244
iter  40 value 87.398220
final  value 87.398126 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.722061 
final  value 94.214007 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.219452 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.651213 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.616427 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 108.010926 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 102.041845 
iter  10 value 92.877855
iter  20 value 87.199042
iter  30 value 86.659959
iter  40 value 86.658513
final  value 86.658512 
converged
Fitting Repeat 2 

# weights:  305
initial  value 114.144497 
iter  10 value 85.971132
iter  20 value 85.951717
iter  20 value 85.951717
iter  20 value 85.951717
final  value 85.951717 
converged
Fitting Repeat 3 

# weights:  305
initial  value 101.164355 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 104.361878 
final  value 94.291892 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.682744 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 103.000049 
iter  10 value 91.902211
iter  20 value 83.584596
iter  30 value 83.583984
iter  40 value 83.583820
iter  40 value 83.583819
iter  40 value 83.583819
final  value 83.583819 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.447351 
final  value 93.809648 
converged
Fitting Repeat 3 

# weights:  507
initial  value 111.852652 
iter  10 value 93.656165
final  value 93.643491 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.974638 
iter  10 value 93.796866
final  value 93.795946 
converged
Fitting Repeat 5 

# weights:  507
initial  value 110.590122 
iter  10 value 92.774174
iter  20 value 86.165571
iter  30 value 85.266993
iter  40 value 84.892070
iter  50 value 84.891462
final  value 84.891458 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.931427 
iter  10 value 94.451644
iter  20 value 93.768953
iter  30 value 93.069432
iter  40 value 93.020598
iter  50 value 93.013171
iter  60 value 93.012245
iter  70 value 86.620831
iter  80 value 84.510340
iter  90 value 83.585768
iter 100 value 83.518931
final  value 83.518931 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 102.369360 
iter  10 value 94.488575
iter  20 value 88.464046
iter  30 value 87.694984
iter  40 value 86.842671
iter  50 value 85.459250
iter  60 value 85.344033
iter  70 value 85.240076
final  value 85.239569 
converged
Fitting Repeat 3 

# weights:  103
initial  value 114.265625 
iter  10 value 94.385331
iter  20 value 93.045984
iter  30 value 91.839315
iter  40 value 90.981410
iter  50 value 90.891773
iter  60 value 90.874496
final  value 90.874455 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.487567 
iter  10 value 94.479456
iter  20 value 93.943325
iter  30 value 93.838021
iter  40 value 93.767392
iter  50 value 93.738328
iter  60 value 84.923971
iter  70 value 82.878238
iter  80 value 80.981196
iter  90 value 80.036129
iter 100 value 79.592165
final  value 79.592165 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.457531 
iter  10 value 94.353595
iter  20 value 93.841149
iter  30 value 93.644289
iter  40 value 85.224884
iter  50 value 84.779419
iter  60 value 84.752477
iter  70 value 84.621421
iter  80 value 84.562076
iter  90 value 83.836064
iter 100 value 83.574393
final  value 83.574393 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 126.494708 
iter  10 value 95.178704
iter  20 value 89.280742
iter  30 value 87.841787
iter  40 value 87.688597
iter  50 value 87.125510
iter  60 value 86.301921
iter  70 value 82.765761
iter  80 value 81.298929
iter  90 value 79.188625
iter 100 value 78.809974
final  value 78.809974 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 110.580089 
iter  10 value 93.526187
iter  20 value 86.228359
iter  30 value 84.626671
iter  40 value 84.223094
iter  50 value 83.943529
iter  60 value 81.058944
iter  70 value 80.222298
iter  80 value 79.694751
iter  90 value 78.889500
iter 100 value 78.442814
final  value 78.442814 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.263878 
iter  10 value 94.743480
iter  20 value 94.412730
iter  30 value 91.253461
iter  40 value 90.247911
iter  50 value 86.914732
iter  60 value 84.321135
iter  70 value 84.146913
iter  80 value 84.129969
iter  90 value 83.595821
iter 100 value 79.965395
final  value 79.965395 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.471334 
iter  10 value 94.454820
iter  20 value 90.070130
iter  30 value 86.511469
iter  40 value 84.233902
iter  50 value 83.998723
iter  60 value 83.349141
iter  70 value 83.178365
iter  80 value 82.898465
iter  90 value 81.158352
iter 100 value 79.456211
final  value 79.456211 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.537343 
iter  10 value 93.957901
iter  20 value 86.244907
iter  30 value 85.639324
iter  40 value 83.999700
iter  50 value 83.171769
iter  60 value 82.790148
iter  70 value 81.205923
iter  80 value 80.615018
iter  90 value 79.918141
iter 100 value 79.280069
final  value 79.280069 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.495616 
iter  10 value 94.642176
iter  20 value 91.619207
iter  30 value 90.321919
iter  40 value 86.500743
iter  50 value 83.124429
iter  60 value 80.969579
iter  70 value 80.441301
iter  80 value 80.056376
iter  90 value 79.992863
iter 100 value 79.896331
final  value 79.896331 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.489769 
iter  10 value 94.590092
iter  20 value 89.301431
iter  30 value 86.843448
iter  40 value 85.839809
iter  50 value 83.783160
iter  60 value 83.106882
iter  70 value 81.053335
iter  80 value 79.196215
iter  90 value 78.510165
iter 100 value 78.315973
final  value 78.315973 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.636004 
iter  10 value 94.356164
iter  20 value 86.086025
iter  30 value 85.649188
iter  40 value 83.049665
iter  50 value 80.948304
iter  60 value 80.366245
iter  70 value 79.609214
iter  80 value 79.239821
iter  90 value 79.195667
iter 100 value 78.956676
final  value 78.956676 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.900843 
iter  10 value 97.017629
iter  20 value 92.880367
iter  30 value 85.390535
iter  40 value 84.701484
iter  50 value 80.943305
iter  60 value 80.260816
iter  70 value 79.886761
iter  80 value 79.622425
iter  90 value 79.399997
iter 100 value 79.195575
final  value 79.195575 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.577997 
iter  10 value 94.556471
iter  20 value 94.089798
iter  30 value 91.214356
iter  40 value 89.501616
iter  50 value 88.215146
iter  60 value 83.545201
iter  70 value 80.736742
iter  80 value 80.334169
iter  90 value 79.996556
iter 100 value 79.663112
final  value 79.663112 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.675802 
final  value 94.485934 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.992952 
final  value 94.485810 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.510917 
iter  10 value 91.682028
iter  20 value 91.501331
iter  30 value 85.306770
iter  40 value 85.166463
iter  50 value 85.154714
iter  60 value 84.792278
final  value 84.725993 
converged
Fitting Repeat 4 

# weights:  103
initial  value 109.554712 
final  value 94.485668 
converged
Fitting Repeat 5 

# weights:  103
initial  value 114.084634 
final  value 94.485935 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.800775 
iter  10 value 94.483054
iter  20 value 94.221846
iter  30 value 84.675885
iter  40 value 82.715600
iter  50 value 82.583070
iter  60 value 82.550682
iter  70 value 82.405083
iter  80 value 82.403203
iter  90 value 82.402959
iter 100 value 82.402617
final  value 82.402617 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 105.676832 
iter  10 value 94.488590
iter  20 value 94.044969
iter  30 value 92.213052
iter  40 value 92.212380
iter  50 value 92.210953
iter  60 value 92.177085
iter  70 value 92.173281
iter  80 value 92.172647
final  value 92.172642 
converged
Fitting Repeat 3 

# weights:  305
initial  value 105.888249 
iter  10 value 94.488429
iter  20 value 94.484344
iter  30 value 94.307611
final  value 94.292578 
converged
Fitting Repeat 4 

# weights:  305
initial  value 105.552862 
iter  10 value 94.489330
iter  20 value 94.484078
iter  30 value 94.129165
iter  40 value 86.799816
iter  50 value 86.790023
iter  60 value 85.248936
iter  70 value 84.800690
iter  80 value 83.632353
iter  90 value 83.202544
iter 100 value 83.154594
final  value 83.154594 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 130.284419 
iter  10 value 94.488930
iter  20 value 94.369929
iter  30 value 86.201555
iter  40 value 86.082108
iter  50 value 85.822903
iter  60 value 85.185662
iter  70 value 85.178752
final  value 85.178034 
converged
Fitting Repeat 1 

# weights:  507
initial  value 108.996608 
iter  10 value 94.299696
iter  20 value 94.290260
iter  30 value 94.176286
iter  40 value 93.748983
iter  50 value 93.451426
final  value 93.434667 
converged
Fitting Repeat 2 

# weights:  507
initial  value 103.273762 
iter  10 value 94.492672
iter  20 value 94.484499
iter  30 value 88.984558
iter  40 value 88.914273
iter  50 value 84.683205
iter  60 value 82.916940
iter  70 value 82.909511
iter  80 value 82.907807
iter  90 value 82.877370
iter 100 value 82.818956
final  value 82.818956 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.602077 
iter  10 value 94.299843
iter  20 value 94.292892
final  value 94.292120 
converged
Fitting Repeat 4 

# weights:  507
initial  value 116.411136 
iter  10 value 94.492755
iter  20 value 94.484656
iter  30 value 86.546026
iter  40 value 86.500888
iter  50 value 85.352133
final  value 85.307103 
converged
Fitting Repeat 5 

# weights:  507
initial  value 105.170429 
iter  10 value 94.491142
iter  20 value 94.479660
iter  30 value 93.946640
iter  40 value 93.458539
final  value 93.364349 
converged
Fitting Repeat 1 

# weights:  507
initial  value 145.531741 
iter  10 value 117.850903
iter  20 value 114.260704
iter  30 value 109.564729
iter  40 value 109.166352
iter  50 value 109.156709
iter  50 value 109.156708
iter  50 value 109.156708
final  value 109.156708 
converged
Fitting Repeat 2 

# weights:  507
initial  value 154.081211 
iter  10 value 117.737766
iter  20 value 116.362571
iter  30 value 105.380428
iter  40 value 105.368640
iter  50 value 105.351278
iter  60 value 105.317480
iter  70 value 103.536387
iter  80 value 102.778194
iter  90 value 102.669547
iter 100 value 102.579392
final  value 102.579392 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 136.104887 
iter  10 value 117.766929
iter  20 value 117.653996
iter  30 value 117.511332
iter  40 value 116.884388
iter  50 value 114.642922
iter  60 value 105.635840
iter  70 value 105.354485
final  value 105.354235 
converged
Fitting Repeat 4 

# weights:  507
initial  value 125.043461 
iter  10 value 117.898748
iter  20 value 117.890357
iter  30 value 114.748948
iter  40 value 108.537044
iter  50 value 108.528186
iter  60 value 105.352701
final  value 105.342087 
converged
Fitting Repeat 5 

# weights:  507
initial  value 120.245861 
iter  10 value 117.767115
iter  20 value 117.678060
iter  30 value 110.857731
iter  40 value 104.682901
iter  50 value 103.034025
iter  60 value 102.624402
final  value 102.623775 
converged
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 -- Thu Mar  5 20:25:41 2026 
*********************************************** 
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 
 20.520   0.486  75.889