##############################################################################
##############################################################################
###
### 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 Ventura 13.7.8
* 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      19.069  0.958  20.677
var_imp       18.975  1.050  21.117
corr_plot     18.808  1.017  20.782
pred_ensembel  6.540  0.155   6.719
enrichfindP    0.196  0.037  10.849
* 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 95.576189 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.349777 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.113083 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.324662 
iter  10 value 93.226533
final  value 93.226191 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.134702 
final  value 93.836066 
converged
Fitting Repeat 1 

# weights:  305
initial  value 117.489599 
final  value 93.836066 
converged
Fitting Repeat 2 

# weights:  305
initial  value 111.270193 
iter  10 value 93.036810
iter  20 value 92.463633
final  value 92.463573 
converged
Fitting Repeat 3 

# weights:  305
initial  value 129.896768 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.461682 
final  value 93.836066 
converged
Fitting Repeat 5 

# weights:  305
initial  value 114.543534 
iter  10 value 94.052910
iter  10 value 94.052910
iter  10 value 94.052910
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 100.659831 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 110.261388 
final  value 93.836066 
converged
Fitting Repeat 3 

# weights:  507
initial  value 108.970646 
iter  10 value 93.836078
final  value 93.836066 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.409764 
iter  10 value 92.892738
iter  10 value 92.892737
iter  10 value 92.892737
final  value 92.892737 
converged
Fitting Repeat 5 

# weights:  507
initial  value 115.396320 
final  value 93.356725 
converged
Fitting Repeat 1 

# weights:  103
initial  value 111.197566 
iter  10 value 94.043971
iter  20 value 93.439447
iter  30 value 93.303172
iter  40 value 88.327883
iter  50 value 86.465923
iter  60 value 83.110161
iter  70 value 82.366405
iter  80 value 82.138892
final  value 82.138806 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.659011 
iter  10 value 94.056635
iter  20 value 93.950352
iter  30 value 93.375901
iter  40 value 93.316869
iter  50 value 93.307476
iter  60 value 93.301420
iter  70 value 91.391750
iter  80 value 88.156352
iter  90 value 87.384052
iter 100 value 82.967789
final  value 82.967789 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 103.466114 
iter  10 value 94.055901
iter  20 value 93.641262
iter  30 value 91.711142
iter  40 value 81.375027
iter  50 value 80.995013
iter  60 value 80.698452
iter  70 value 79.312685
iter  80 value 78.977852
iter  90 value 78.864155
iter 100 value 78.730170
final  value 78.730170 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.222238 
iter  10 value 94.149862
iter  20 value 89.893524
iter  30 value 83.500036
iter  40 value 82.883722
iter  50 value 82.262987
iter  60 value 81.617471
final  value 81.608310 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.057498 
iter  10 value 94.128540
iter  20 value 88.969011
iter  30 value 88.386378
iter  40 value 85.433845
iter  50 value 84.972418
iter  60 value 81.004484
iter  70 value 79.257713
iter  80 value 78.694038
iter  90 value 78.521719
iter 100 value 78.426634
final  value 78.426634 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 108.534317 
iter  10 value 93.905581
iter  20 value 92.207578
iter  30 value 89.665247
iter  40 value 87.370314
iter  50 value 84.332398
iter  60 value 79.987416
iter  70 value 79.441949
iter  80 value 78.257303
iter  90 value 76.966913
iter 100 value 76.742003
final  value 76.742003 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 105.082632 
iter  10 value 94.132369
iter  20 value 93.662615
iter  30 value 93.451732
iter  40 value 92.887924
iter  50 value 92.384437
iter  60 value 85.081121
iter  70 value 82.149281
iter  80 value 79.470703
iter  90 value 78.623800
iter 100 value 78.103725
final  value 78.103725 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 108.162874 
iter  10 value 93.749054
iter  20 value 86.703190
iter  30 value 82.753618
iter  40 value 82.454744
iter  50 value 81.998262
iter  60 value 81.877623
iter  70 value 81.825263
iter  80 value 81.646373
iter  90 value 79.702488
iter 100 value 78.435556
final  value 78.435556 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 104.791996 
iter  10 value 94.054816
iter  20 value 93.340251
iter  30 value 84.052881
iter  40 value 82.005886
iter  50 value 80.845188
iter  60 value 78.848724
iter  70 value 77.631222
iter  80 value 77.354104
iter  90 value 76.973290
iter 100 value 76.753504
final  value 76.753504 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.968769 
iter  10 value 94.195132
iter  20 value 90.084441
iter  30 value 86.214072
iter  40 value 85.062585
iter  50 value 83.280945
iter  60 value 82.903610
iter  70 value 82.454563
iter  80 value 82.272590
iter  90 value 82.013593
iter 100 value 80.832369
final  value 80.832369 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 110.540235 
iter  10 value 87.290710
iter  20 value 86.252567
iter  30 value 82.919268
iter  40 value 81.598432
iter  50 value 80.299273
iter  60 value 80.103862
iter  70 value 79.135290
iter  80 value 78.770271
iter  90 value 78.449977
iter 100 value 78.270227
final  value 78.270227 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 117.716599 
iter  10 value 94.242370
iter  20 value 85.039992
iter  30 value 84.069577
iter  40 value 83.428834
iter  50 value 82.659051
iter  60 value 78.783067
iter  70 value 77.956458
iter  80 value 77.846375
iter  90 value 77.452056
iter 100 value 77.087337
final  value 77.087337 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 102.348612 
iter  10 value 91.763997
iter  20 value 86.574273
iter  30 value 85.014120
iter  40 value 84.471240
iter  50 value 84.177304
iter  60 value 84.119076
iter  70 value 83.050170
iter  80 value 81.103469
iter  90 value 79.340521
iter 100 value 78.089462
final  value 78.089462 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 112.322181 
iter  10 value 94.675257
iter  20 value 90.144701
iter  30 value 83.026365
iter  40 value 80.226189
iter  50 value 79.027075
iter  60 value 78.106252
iter  70 value 77.698193
iter  80 value 77.590390
iter  90 value 77.473363
iter 100 value 77.197938
final  value 77.197938 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.745645 
iter  10 value 94.094622
iter  20 value 93.086117
iter  30 value 87.222769
iter  40 value 81.727961
iter  50 value 79.633853
iter  60 value 78.401735
iter  70 value 77.822696
iter  80 value 77.716779
iter  90 value 77.348442
iter 100 value 77.062615
final  value 77.062615 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.900415 
iter  10 value 94.054660
iter  20 value 94.052920
final  value 94.052914 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.386445 
final  value 94.054504 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.994780 
iter  10 value 94.054777
iter  20 value 94.052918
iter  20 value 94.052917
iter  20 value 94.052917
final  value 94.052917 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.699255 
final  value 94.054928 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.445026 
final  value 94.054505 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.437596 
iter  10 value 93.840840
iter  20 value 93.762996
iter  30 value 92.419663
iter  40 value 90.170232
iter  50 value 90.170060
iter  60 value 90.169851
final  value 90.169307 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.371157 
iter  10 value 93.840965
iter  20 value 93.361995
final  value 93.357227 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.165104 
iter  10 value 93.841155
iter  20 value 93.420201
final  value 93.357582 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.127214 
iter  10 value 94.057457
iter  20 value 94.052927
final  value 94.052920 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.434600 
iter  10 value 93.841473
iter  20 value 93.837381
iter  30 value 93.827423
iter  40 value 93.535628
iter  50 value 89.299052
iter  60 value 81.130003
iter  70 value 77.898870
iter  80 value 77.811332
iter  90 value 77.809889
iter 100 value 77.809429
final  value 77.809429 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 114.769041 
iter  10 value 93.612401
iter  20 value 93.605493
final  value 93.605377 
converged
Fitting Repeat 2 

# weights:  507
initial  value 120.810040 
iter  10 value 93.844632
iter  20 value 93.840698
iter  30 value 93.837133
iter  40 value 93.724909
iter  50 value 86.585889
iter  60 value 79.996982
iter  70 value 77.388990
iter  80 value 76.971722
iter  90 value 75.985184
iter 100 value 75.487390
final  value 75.487390 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 98.665066 
iter  10 value 93.966343
iter  20 value 93.844326
iter  30 value 90.314364
iter  40 value 83.560040
iter  50 value 83.031798
iter  60 value 82.870259
iter  70 value 82.859991
final  value 82.859987 
converged
Fitting Repeat 4 

# weights:  507
initial  value 110.604029 
iter  10 value 94.060428
iter  20 value 93.063392
iter  30 value 83.259765
iter  40 value 81.937905
iter  50 value 81.932198
iter  60 value 81.913463
iter  70 value 81.720502
iter  80 value 81.364572
iter  90 value 81.276261
iter 100 value 81.257533
final  value 81.257533 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 99.467335 
iter  10 value 92.725261
iter  20 value 91.377905
iter  30 value 91.088065
iter  40 value 91.066721
final  value 90.954376 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.217937 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.130885 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 108.508571 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.444645 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.689698 
final  value 94.483810 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.328914 
final  value 94.354396 
converged
Fitting Repeat 2 

# weights:  305
initial  value 109.511937 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.687591 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.923009 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 102.383277 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 109.881478 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.095165 
iter  10 value 94.338497
final  value 94.308192 
converged
Fitting Repeat 3 

# weights:  507
initial  value 98.035255 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.855652 
final  value 94.195714 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.064378 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 107.394836 
iter  10 value 94.490493
iter  20 value 90.541493
iter  30 value 87.666484
iter  40 value 87.167507
iter  50 value 86.243557
iter  60 value 85.950494
iter  70 value 85.948103
iter  80 value 85.945528
final  value 85.945525 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.498020 
iter  10 value 94.284651
iter  20 value 91.143925
iter  30 value 86.093963
iter  40 value 85.848455
iter  50 value 84.632091
iter  60 value 84.085326
iter  70 value 83.551101
iter  80 value 83.300246
iter  90 value 83.096799
final  value 83.096485 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.310632 
iter  10 value 94.479853
iter  20 value 94.381941
iter  30 value 93.923125
iter  40 value 91.890575
iter  50 value 91.135379
iter  60 value 86.973590
iter  70 value 85.929282
iter  80 value 85.516899
iter  90 value 85.114027
iter 100 value 85.064159
final  value 85.064159 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 105.651564 
iter  10 value 94.480305
iter  20 value 92.111797
iter  30 value 91.470251
iter  40 value 87.753898
iter  50 value 87.240724
iter  60 value 86.408974
iter  70 value 86.196978
iter  80 value 86.058677
iter  90 value 85.606346
iter 100 value 85.365126
final  value 85.365126 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 103.888742 
iter  10 value 93.840239
iter  20 value 86.904920
iter  30 value 86.231020
iter  40 value 86.031193
iter  50 value 85.792168
iter  60 value 85.371083
iter  70 value 84.976543
iter  80 value 84.905767
final  value 84.904573 
converged
Fitting Repeat 1 

# weights:  305
initial  value 107.205581 
iter  10 value 94.486629
iter  20 value 87.142206
iter  30 value 86.756446
iter  40 value 86.672916
iter  50 value 86.342276
iter  60 value 86.291396
iter  70 value 86.022106
iter  80 value 85.578368
iter  90 value 84.929491
iter 100 value 83.699434
final  value 83.699434 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 119.159512 
iter  10 value 94.579665
iter  20 value 92.555205
iter  30 value 88.909848
iter  40 value 88.183461
iter  50 value 87.278548
iter  60 value 87.009438
iter  70 value 85.262689
iter  80 value 83.568089
iter  90 value 82.791307
iter 100 value 82.478684
final  value 82.478684 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.764685 
iter  10 value 94.500679
iter  20 value 94.383398
iter  30 value 94.088447
iter  40 value 93.279021
iter  50 value 89.515309
iter  60 value 89.095483
iter  70 value 87.163663
iter  80 value 86.041087
iter  90 value 85.902346
iter 100 value 85.775405
final  value 85.775405 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 109.418630 
iter  10 value 94.425903
iter  20 value 88.906129
iter  30 value 87.414775
iter  40 value 87.109913
iter  50 value 86.580320
iter  60 value 85.724434
iter  70 value 84.872930
iter  80 value 83.940132
iter  90 value 82.878877
iter 100 value 82.314885
final  value 82.314885 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 110.958758 
iter  10 value 94.349188
iter  20 value 87.860428
iter  30 value 86.532935
iter  40 value 86.295040
iter  50 value 86.211564
iter  60 value 85.562311
iter  70 value 85.265069
iter  80 value 85.204934
iter  90 value 85.062248
iter 100 value 84.204623
final  value 84.204623 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 111.454013 
iter  10 value 92.740190
iter  20 value 86.965996
iter  30 value 85.130240
iter  40 value 82.739381
iter  50 value 82.138416
iter  60 value 81.962121
iter  70 value 81.793065
iter  80 value 81.754306
iter  90 value 81.732822
iter 100 value 81.709757
final  value 81.709757 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.961481 
iter  10 value 94.653201
iter  20 value 90.459302
iter  30 value 88.923308
iter  40 value 85.711322
iter  50 value 83.573495
iter  60 value 83.051346
iter  70 value 82.481590
iter  80 value 82.036857
iter  90 value 81.956794
iter 100 value 81.883830
final  value 81.883830 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.681473 
iter  10 value 94.489907
iter  20 value 87.757001
iter  30 value 85.608401
iter  40 value 83.990045
iter  50 value 83.149459
iter  60 value 82.646013
iter  70 value 82.448471
iter  80 value 82.317324
iter  90 value 82.275308
iter 100 value 82.235504
final  value 82.235504 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.853417 
iter  10 value 94.587490
iter  20 value 90.097875
iter  30 value 88.594202
iter  40 value 85.405925
iter  50 value 84.256126
iter  60 value 82.936310
iter  70 value 82.185796
iter  80 value 82.025078
iter  90 value 81.747477
iter 100 value 81.744760
final  value 81.744760 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 104.491470 
iter  10 value 94.933240
iter  20 value 94.473884
iter  30 value 93.382881
iter  40 value 89.682906
iter  50 value 85.570138
iter  60 value 83.364656
iter  70 value 82.717847
iter  80 value 82.618143
iter  90 value 82.319966
iter 100 value 82.010530
final  value 82.010530 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 103.897954 
final  value 94.485750 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.271150 
final  value 94.485643 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.011798 
final  value 94.486222 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.026222 
final  value 94.485805 
converged
Fitting Repeat 5 

# weights:  103
initial  value 111.325037 
iter  10 value 94.485721
final  value 94.484214 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.160528 
iter  10 value 88.391168
iter  20 value 88.293371
iter  30 value 88.292827
iter  40 value 88.291234
iter  50 value 88.290008
iter  60 value 88.289485
iter  70 value 87.970229
final  value 87.970034 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.259233 
iter  10 value 94.486306
iter  20 value 93.095236
iter  30 value 91.946567
iter  40 value 91.932446
iter  50 value 91.932051
iter  60 value 91.931167
iter  70 value 90.522258
iter  80 value 90.521189
final  value 90.521100 
converged
Fitting Repeat 3 

# weights:  305
initial  value 93.491481 
iter  10 value 87.130724
iter  20 value 85.646064
final  value 85.623350 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.569950 
iter  10 value 94.489248
iter  20 value 91.295344
iter  30 value 84.546934
iter  40 value 84.516703
iter  40 value 84.516703
final  value 84.516703 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.635897 
iter  10 value 94.488662
iter  20 value 93.753001
iter  30 value 93.671330
iter  40 value 93.202696
final  value 93.190377 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.458676 
iter  10 value 94.366393
iter  20 value 94.312984
iter  30 value 94.303454
final  value 94.289799 
converged
Fitting Repeat 2 

# weights:  507
initial  value 98.424302 
iter  10 value 94.488964
iter  20 value 94.355915
iter  30 value 86.067270
iter  40 value 85.456021
iter  50 value 81.922361
iter  60 value 81.832381
iter  70 value 81.831986
final  value 81.831601 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.961281 
iter  10 value 94.362676
iter  20 value 93.755688
iter  30 value 88.185790
iter  40 value 83.428224
iter  50 value 82.174447
iter  60 value 82.113198
iter  70 value 82.112534
iter  70 value 82.112533
final  value 82.112533 
converged
Fitting Repeat 4 

# weights:  507
initial  value 105.335996 
iter  10 value 94.451324
iter  20 value 94.402024
iter  30 value 90.222970
iter  40 value 86.517939
iter  50 value 85.805818
iter  60 value 85.735066
final  value 85.733437 
converged
Fitting Repeat 5 

# weights:  507
initial  value 111.586325 
iter  10 value 94.492457
iter  20 value 94.443182
final  value 94.354746 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.813808 
final  value 94.026542 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.123490 
final  value 94.448052 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.381957 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.147476 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 105.104923 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.514046 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 103.499741 
final  value 94.026542 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.319707 
final  value 94.026542 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.000852 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.011756 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 114.054090 
iter  10 value 94.026542
iter  10 value 94.026542
iter  10 value 94.026542
final  value 94.026542 
converged
Fitting Repeat 2 

# weights:  507
initial  value 96.092226 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 130.876283 
iter  10 value 94.026967
final  value 94.026542 
converged
Fitting Repeat 4 

# weights:  507
initial  value 112.600978 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 104.602397 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.488535 
iter  10 value 94.452524
iter  20 value 94.261895
iter  30 value 89.193588
iter  40 value 86.351426
iter  50 value 84.942647
iter  60 value 84.355222
iter  70 value 84.158062
iter  80 value 84.129846
final  value 84.129840 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.329721 
iter  10 value 89.492889
iter  20 value 89.062174
iter  30 value 88.916750
iter  40 value 86.466464
iter  50 value 85.659591
iter  60 value 85.125773
iter  70 value 84.894919
final  value 84.894898 
converged
Fitting Repeat 3 

# weights:  103
initial  value 104.230730 
iter  10 value 94.007798
iter  20 value 92.007614
iter  30 value 88.784796
iter  40 value 88.020492
iter  50 value 87.741600
iter  60 value 83.808077
iter  70 value 82.352142
iter  80 value 82.320496
final  value 82.320447 
converged
Fitting Repeat 4 

# weights:  103
initial  value 117.205548 
iter  10 value 94.467507
iter  20 value 93.588049
iter  30 value 93.150226
iter  40 value 92.892195
iter  50 value 92.887963
iter  60 value 92.879712
iter  70 value 89.325130
iter  80 value 85.983243
iter  90 value 85.265415
iter 100 value 84.563223
final  value 84.563223 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 101.753271 
iter  10 value 94.475690
iter  20 value 93.921000
iter  30 value 93.714159
iter  40 value 93.663696
iter  50 value 93.362557
iter  60 value 89.063898
iter  70 value 88.517678
iter  80 value 88.041741
iter  90 value 85.353191
iter 100 value 84.127948
final  value 84.127948 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 114.647991 
iter  10 value 93.371940
iter  20 value 88.044021
iter  30 value 85.937573
iter  40 value 85.554363
iter  50 value 85.022477
iter  60 value 82.837707
iter  70 value 82.096655
iter  80 value 81.983812
iter  90 value 81.259947
iter 100 value 80.934547
final  value 80.934547 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 134.889287 
iter  10 value 95.345446
iter  20 value 91.402070
iter  30 value 88.893204
iter  40 value 88.495467
iter  50 value 86.011623
iter  60 value 85.223491
iter  70 value 84.308734
iter  80 value 84.073847
iter  90 value 84.013355
iter 100 value 83.367625
final  value 83.367625 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 100.942004 
iter  10 value 94.857868
iter  20 value 91.071399
iter  30 value 87.855207
iter  40 value 86.432397
iter  50 value 86.079584
iter  60 value 85.226608
iter  70 value 84.758738
iter  80 value 84.697580
iter  90 value 84.197479
iter 100 value 81.738444
final  value 81.738444 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 117.279086 
iter  10 value 94.456041
iter  20 value 93.076413
iter  30 value 86.352715
iter  40 value 85.290427
iter  50 value 84.766241
iter  60 value 84.755279
iter  70 value 84.727355
iter  80 value 84.496054
iter  90 value 83.873220
iter 100 value 83.239076
final  value 83.239076 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 107.076418 
iter  10 value 94.890125
iter  20 value 92.686967
iter  30 value 88.204194
iter  40 value 86.256589
iter  50 value 85.564083
iter  60 value 82.952096
iter  70 value 82.371403
iter  80 value 81.930578
iter  90 value 81.217115
iter 100 value 81.127503
final  value 81.127503 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.594580 
iter  10 value 94.659135
iter  20 value 94.354232
iter  30 value 93.674449
iter  40 value 92.110700
iter  50 value 88.008220
iter  60 value 86.091397
iter  70 value 83.864343
iter  80 value 82.973461
iter  90 value 81.798056
iter 100 value 80.860915
final  value 80.860915 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.012152 
iter  10 value 95.069962
iter  20 value 93.414115
iter  30 value 89.241691
iter  40 value 85.482904
iter  50 value 85.068571
iter  60 value 83.323616
iter  70 value 81.617319
iter  80 value 81.358279
iter  90 value 81.188598
iter 100 value 81.099088
final  value 81.099088 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 116.927424 
iter  10 value 94.569355
iter  20 value 87.586018
iter  30 value 85.879271
iter  40 value 84.045328
iter  50 value 83.681390
iter  60 value 82.760086
iter  70 value 82.487038
iter  80 value 82.269628
iter  90 value 82.079218
iter 100 value 81.965860
final  value 81.965860 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 110.347234 
iter  10 value 95.422756
iter  20 value 85.851874
iter  30 value 85.446917
iter  40 value 83.801744
iter  50 value 82.915926
iter  60 value 81.798938
iter  70 value 81.432217
iter  80 value 81.023038
iter  90 value 80.947189
iter 100 value 80.927423
final  value 80.927423 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.584281 
iter  10 value 94.576992
iter  20 value 93.507232
iter  30 value 90.729686
iter  40 value 86.221032
iter  50 value 83.996134
iter  60 value 83.228488
iter  70 value 82.178019
iter  80 value 81.798330
iter  90 value 81.275471
iter 100 value 81.056842
final  value 81.056842 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.121532 
iter  10 value 94.028678
iter  20 value 94.027210
iter  30 value 93.593782
iter  40 value 88.466547
iter  50 value 84.102390
iter  60 value 83.682643
iter  70 value 83.492227
iter  80 value 83.423816
final  value 83.423584 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.907207 
final  value 94.499434 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.802833 
iter  10 value 94.028345
iter  20 value 93.323336
final  value 93.320528 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.562611 
iter  10 value 94.485840
final  value 94.484470 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.295527 
iter  10 value 94.486086
iter  20 value 94.484285
final  value 94.484216 
converged
Fitting Repeat 1 

# weights:  305
initial  value 105.294188 
iter  10 value 94.489247
iter  20 value 94.484224
iter  30 value 93.558131
iter  40 value 89.476677
iter  50 value 88.561085
iter  60 value 88.518625
iter  70 value 88.160437
iter  80 value 88.149255
final  value 88.148682 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.958627 
iter  10 value 94.488794
iter  20 value 91.860540
iter  30 value 86.144150
final  value 86.144113 
converged
Fitting Repeat 3 

# weights:  305
initial  value 102.760507 
iter  10 value 94.432359
iter  20 value 94.006074
iter  30 value 94.003315
iter  40 value 93.926328
iter  40 value 93.926327
iter  50 value 86.607413
iter  60 value 84.564704
iter  70 value 84.056881
final  value 84.046944 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.589050 
iter  10 value 94.031916
iter  20 value 93.213110
iter  30 value 92.868217
iter  40 value 92.261303
iter  50 value 85.655614
iter  60 value 84.509088
iter  70 value 82.564545
iter  80 value 82.533295
iter  90 value 82.532696
iter  90 value 82.532696
iter  90 value 82.532696
final  value 82.532696 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.988228 
iter  10 value 94.487830
iter  20 value 94.328874
iter  30 value 93.696442
iter  40 value 93.321003
final  value 93.320604 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.411101 
iter  10 value 93.350536
iter  20 value 89.909545
iter  30 value 84.294343
iter  40 value 82.601331
iter  50 value 82.551740
iter  60 value 82.550136
iter  60 value 82.550136
final  value 82.550136 
converged
Fitting Repeat 2 

# weights:  507
initial  value 114.913633 
iter  10 value 94.456299
iter  20 value 94.033914
iter  30 value 94.028494
iter  40 value 89.755913
iter  50 value 86.219306
iter  60 value 85.497454
iter  70 value 85.018961
final  value 84.990783 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.526354 
iter  10 value 94.020297
iter  20 value 93.673161
iter  30 value 93.671736
iter  40 value 93.666732
iter  50 value 88.909110
iter  60 value 87.079402
iter  70 value 85.749817
iter  80 value 85.672727
iter  90 value 84.870805
iter 100 value 84.855705
final  value 84.855705 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 101.494977 
iter  10 value 89.984876
iter  20 value 84.970785
iter  30 value 84.287944
iter  40 value 83.252675
iter  50 value 81.249608
iter  60 value 81.036083
iter  70 value 81.024653
iter  80 value 81.022426
final  value 81.020343 
converged
Fitting Repeat 5 

# weights:  507
initial  value 100.698338 
iter  10 value 94.491778
iter  20 value 94.433007
iter  30 value 92.646426
iter  40 value 92.642306
iter  50 value 92.638094
iter  60 value 92.557734
iter  70 value 89.025972
iter  80 value 84.159433
iter  90 value 83.246211
iter 100 value 83.219594
final  value 83.219594 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 100.816756 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.298911 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.806052 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 106.885554 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.145139 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.976809 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 101.171407 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 112.755678 
final  value 94.052911 
converged
Fitting Repeat 4 

# weights:  305
initial  value 106.011386 
final  value 94.008696 
converged
Fitting Repeat 5 

# weights:  305
initial  value 104.831271 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 132.661535 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.097972 
iter  10 value 93.950928
final  value 93.810010 
converged
Fitting Repeat 3 

# weights:  507
initial  value 104.703097 
final  value 94.008696 
converged
Fitting Repeat 4 

# weights:  507
initial  value 117.336369 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 123.011307 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.488930 
iter  10 value 93.953694
iter  20 value 93.472614
iter  30 value 90.105218
iter  40 value 84.735958
iter  50 value 83.298355
iter  60 value 82.700982
iter  70 value 82.592123
iter  80 value 82.555759
final  value 82.555513 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.170560 
iter  10 value 93.778067
iter  20 value 93.444055
iter  30 value 90.752094
iter  40 value 86.809685
iter  50 value 83.368004
iter  60 value 82.337311
iter  70 value 82.124692
iter  80 value 82.102632
final  value 82.102624 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.396119 
iter  10 value 93.982757
iter  20 value 93.448753
iter  30 value 87.214877
iter  40 value 86.458047
iter  50 value 84.024834
iter  60 value 83.493445
iter  70 value 82.500761
iter  80 value 82.198656
iter  90 value 82.132836
iter 100 value 82.110090
final  value 82.110090 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 114.520063 
iter  10 value 93.566502
iter  20 value 83.957050
iter  30 value 83.608209
iter  40 value 83.145698
iter  50 value 82.607637
iter  60 value 82.557566
final  value 82.555514 
converged
Fitting Repeat 5 

# weights:  103
initial  value 97.702920 
iter  10 value 94.055380
iter  20 value 90.621530
iter  30 value 84.449821
iter  40 value 84.221486
iter  50 value 82.748246
iter  60 value 82.679173
iter  70 value 82.664719
iter  80 value 82.651883
iter  90 value 82.556318
final  value 82.555513 
converged
Fitting Repeat 1 

# weights:  305
initial  value 106.216573 
iter  10 value 94.184020
iter  20 value 91.420422
iter  30 value 86.109862
iter  40 value 84.275806
iter  50 value 82.390142
iter  60 value 82.180005
iter  70 value 81.993870
iter  80 value 80.576477
iter  90 value 80.259613
iter 100 value 80.173932
final  value 80.173932 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 99.419072 
iter  10 value 93.457372
iter  20 value 89.381260
iter  30 value 87.471921
iter  40 value 85.934554
iter  50 value 82.510038
iter  60 value 80.944600
iter  70 value 80.570954
iter  80 value 80.331763
iter  90 value 80.092113
iter 100 value 79.687102
final  value 79.687102 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 110.004451 
iter  10 value 93.995044
iter  20 value 87.225151
iter  30 value 84.003265
iter  40 value 83.197573
iter  50 value 82.740873
iter  60 value 82.703102
iter  70 value 82.375759
iter  80 value 81.370217
iter  90 value 80.695544
iter 100 value 80.065846
final  value 80.065846 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 102.873317 
iter  10 value 93.636628
iter  20 value 84.628714
iter  30 value 84.154481
iter  40 value 82.848493
iter  50 value 81.614700
iter  60 value 81.103889
iter  70 value 80.386589
iter  80 value 80.150830
iter  90 value 80.097906
iter 100 value 79.969783
final  value 79.969783 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 120.959080 
iter  10 value 94.508516
iter  20 value 87.128676
iter  30 value 82.653002
iter  40 value 82.534451
iter  50 value 81.837732
iter  60 value 80.678100
iter  70 value 80.245828
iter  80 value 80.149011
iter  90 value 80.059810
iter 100 value 80.041547
final  value 80.041547 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.299779 
iter  10 value 93.781946
iter  20 value 85.809882
iter  30 value 83.141204
iter  40 value 81.123370
iter  50 value 80.528282
iter  60 value 80.321472
iter  70 value 80.251392
iter  80 value 80.001029
iter  90 value 79.956750
iter 100 value 79.858685
final  value 79.858685 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.018912 
iter  10 value 89.610635
iter  20 value 86.948969
iter  30 value 86.260755
iter  40 value 84.862187
iter  50 value 82.217447
iter  60 value 81.487012
iter  70 value 81.253276
iter  80 value 81.154919
iter  90 value 80.924435
iter 100 value 80.891290
final  value 80.891290 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 101.813182 
iter  10 value 93.958487
iter  20 value 87.304348
iter  30 value 85.661704
iter  40 value 82.847583
iter  50 value 82.172324
iter  60 value 81.873675
iter  70 value 81.615205
iter  80 value 80.826160
iter  90 value 79.903877
iter 100 value 79.603254
final  value 79.603254 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 120.711944 
iter  10 value 93.959162
iter  20 value 87.423195
iter  30 value 85.415493
iter  40 value 82.347097
iter  50 value 81.915652
iter  60 value 81.596101
iter  70 value 81.122641
iter  80 value 80.408266
iter  90 value 80.210292
iter 100 value 80.120306
final  value 80.120306 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 127.793179 
iter  10 value 94.452641
iter  20 value 94.032754
iter  30 value 93.196933
iter  40 value 84.135890
iter  50 value 83.933330
iter  60 value 82.847912
iter  70 value 80.839659
iter  80 value 80.629942
iter  90 value 80.432068
iter 100 value 80.153515
final  value 80.153515 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 102.884642 
final  value 94.054561 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.760753 
iter  10 value 94.054321
iter  20 value 94.049791
iter  30 value 94.009093
final  value 94.008785 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.805139 
iter  10 value 93.970629
iter  20 value 93.808556
iter  30 value 93.806395
final  value 93.805796 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.066895 
final  value 94.054677 
converged
Fitting Repeat 5 

# weights:  103
initial  value 109.504048 
final  value 94.054314 
converged
Fitting Repeat 1 

# weights:  305
initial  value 95.478907 
iter  10 value 94.057937
iter  20 value 93.050975
iter  30 value 83.304302
iter  40 value 83.225899
iter  50 value 83.218995
final  value 83.218758 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.479658 
iter  10 value 94.057345
iter  20 value 94.052906
iter  30 value 93.773977
iter  40 value 82.885801
iter  50 value 82.883802
iter  60 value 82.877909
iter  70 value 82.873815
iter  80 value 82.873504
iter  90 value 82.873323
iter 100 value 82.872473
final  value 82.872473 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 99.248070 
iter  10 value 94.058063
iter  20 value 94.053562
iter  30 value 93.808362
iter  40 value 93.805533
iter  50 value 93.753909
final  value 93.753610 
converged
Fitting Repeat 4 

# weights:  305
initial  value 103.506337 
iter  10 value 94.057683
iter  20 value 94.052964
final  value 94.052951 
converged
Fitting Repeat 5 

# weights:  305
initial  value 106.120394 
iter  10 value 94.055383
iter  20 value 94.047502
iter  30 value 94.013857
iter  40 value 94.009649
iter  50 value 93.964189
iter  60 value 93.963221
final  value 93.963165 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.765589 
iter  10 value 93.647766
iter  20 value 93.644354
iter  30 value 93.641346
iter  40 value 93.638252
iter  50 value 93.609717
iter  60 value 93.608942
iter  70 value 93.608075
iter  80 value 93.607225
iter  90 value 92.719773
iter 100 value 90.300017
final  value 90.300017 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 91.683268 
iter  10 value 86.407010
iter  20 value 85.985321
iter  30 value 85.979637
iter  40 value 85.906945
final  value 85.906774 
converged
Fitting Repeat 3 

# weights:  507
initial  value 122.824864 
iter  10 value 94.061203
iter  20 value 94.053269
iter  30 value 83.240491
final  value 83.236379 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.999970 
iter  10 value 94.059413
iter  20 value 84.175752
iter  30 value 82.266872
iter  40 value 80.003124
iter  50 value 78.875208
iter  60 value 78.775782
iter  70 value 78.772435
iter  80 value 78.678523
iter  90 value 78.470162
iter 100 value 78.239690
final  value 78.239690 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 108.565202 
iter  10 value 94.060965
iter  20 value 94.049386
iter  30 value 93.805320
iter  40 value 93.805052
final  value 93.805044 
converged
Fitting Repeat 1 

# weights:  103
initial  value 98.568229 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.403834 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 95.261373 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.524981 
iter  10 value 93.947312
iter  10 value 93.947312
iter  10 value 93.947312
final  value 93.947312 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.574727 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.652927 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 97.573488 
final  value 94.467391 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.382187 
iter  10 value 94.269759
iter  20 value 87.613704
iter  30 value 87.298273
final  value 87.298269 
converged
Fitting Repeat 4 

# weights:  305
initial  value 99.482365 
iter  10 value 94.267949
final  value 93.879755 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.010113 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.860274 
iter  10 value 94.113372
final  value 94.113276 
converged
Fitting Repeat 2 

# weights:  507
initial  value 124.426553 
iter  10 value 94.484211
iter  10 value 94.484211
iter  10 value 94.484211
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 100.329471 
iter  10 value 94.350886
iter  20 value 85.516009
iter  30 value 85.503385
iter  40 value 85.503175
final  value 85.503170 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.742069 
iter  10 value 94.219352
iter  20 value 94.113303
final  value 94.113277 
converged
Fitting Repeat 5 

# weights:  507
initial  value 103.989589 
iter  10 value 94.424858
iter  20 value 94.154289
final  value 94.154286 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.052272 
iter  10 value 94.431441
iter  20 value 94.249443
iter  30 value 88.571334
iter  40 value 87.632903
iter  50 value 87.327629
iter  60 value 84.415771
iter  70 value 83.526525
iter  80 value 83.175330
iter  90 value 82.836258
iter 100 value 82.752473
final  value 82.752473 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.264891 
iter  10 value 94.346718
iter  20 value 90.854975
iter  30 value 88.116084
iter  40 value 85.270400
iter  50 value 84.654915
iter  60 value 83.907684
iter  70 value 83.737823
iter  80 value 83.566680
iter  90 value 83.442520
iter 100 value 83.430789
final  value 83.430789 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 106.180259 
iter  10 value 93.960289
iter  20 value 85.864089
iter  30 value 85.308790
iter  40 value 85.009483
iter  50 value 84.779125
iter  60 value 84.450461
final  value 84.446395 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.517020 
iter  10 value 94.495374
iter  20 value 94.466014
iter  30 value 91.247479
iter  40 value 87.257335
iter  50 value 86.395282
iter  60 value 86.251355
iter  70 value 84.888195
iter  80 value 84.318181
iter  90 value 84.290698
iter 100 value 84.272737
final  value 84.272737 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 95.965567 
iter  10 value 94.488629
iter  20 value 93.806758
iter  30 value 88.575262
iter  40 value 87.616884
iter  50 value 84.760151
iter  60 value 83.080036
iter  70 value 82.869705
iter  80 value 82.723588
iter  90 value 82.704115
iter  90 value 82.704115
iter  90 value 82.704115
final  value 82.704115 
converged
Fitting Repeat 1 

# weights:  305
initial  value 115.357925 
iter  10 value 94.955421
iter  20 value 94.284436
iter  30 value 87.169717
iter  40 value 87.027022
iter  50 value 86.465737
iter  60 value 83.481313
iter  70 value 82.896322
iter  80 value 82.778046
iter  90 value 82.771998
iter 100 value 82.484320
final  value 82.484320 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 117.350204 
iter  10 value 94.466370
iter  20 value 92.988551
iter  30 value 88.116236
iter  40 value 86.259270
iter  50 value 85.026823
iter  60 value 83.734737
iter  70 value 82.750626
iter  80 value 81.706112
iter  90 value 81.632363
iter 100 value 81.510647
final  value 81.510647 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 115.252106 
iter  10 value 94.568763
iter  20 value 93.796512
iter  30 value 92.644534
iter  40 value 92.204856
iter  50 value 85.332427
iter  60 value 83.911410
iter  70 value 83.821306
iter  80 value 83.749969
iter  90 value 83.651337
iter 100 value 83.407997
final  value 83.407997 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 101.386674 
iter  10 value 93.496536
iter  20 value 92.972647
iter  30 value 91.338769
iter  40 value 86.235174
iter  50 value 83.958337
iter  60 value 83.823545
iter  70 value 83.543375
iter  80 value 83.407404
iter  90 value 83.145164
iter 100 value 82.873734
final  value 82.873734 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 106.052868 
iter  10 value 94.514649
iter  20 value 94.210574
iter  30 value 87.300095
iter  40 value 85.324185
iter  50 value 84.054710
iter  60 value 83.610058
iter  70 value 82.417177
iter  80 value 82.172149
iter  90 value 81.884757
iter 100 value 81.689537
final  value 81.689537 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 104.008907 
iter  10 value 94.822670
iter  20 value 94.357793
iter  30 value 92.988638
iter  40 value 88.949587
iter  50 value 84.231769
iter  60 value 83.912903
iter  70 value 83.225817
iter  80 value 82.287646
iter  90 value 81.465148
iter 100 value 81.210345
final  value 81.210345 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 132.647256 
iter  10 value 94.475814
iter  20 value 89.397794
iter  30 value 88.505542
iter  40 value 85.579728
iter  50 value 84.937926
iter  60 value 84.215022
iter  70 value 83.427460
iter  80 value 83.138024
iter  90 value 81.761255
iter 100 value 81.323624
final  value 81.323624 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 124.091737 
iter  10 value 94.065249
iter  20 value 86.706641
iter  30 value 86.207437
iter  40 value 84.490685
iter  50 value 83.326379
iter  60 value 82.816690
iter  70 value 82.703265
iter  80 value 82.331538
iter  90 value 82.037483
iter 100 value 81.951072
final  value 81.951072 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.782997 
iter  10 value 94.464947
iter  20 value 91.109205
iter  30 value 84.217036
iter  40 value 83.882933
iter  50 value 83.035843
iter  60 value 82.365487
iter  70 value 81.849159
iter  80 value 81.773261
iter  90 value 81.754959
iter 100 value 81.716724
final  value 81.716724 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.605612 
iter  10 value 91.453604
iter  20 value 85.812534
iter  30 value 83.923687
iter  40 value 82.904508
iter  50 value 82.594453
iter  60 value 81.820008
iter  70 value 81.567469
iter  80 value 81.108175
iter  90 value 80.931373
iter 100 value 80.793338
final  value 80.793338 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 112.543713 
final  value 94.485917 
converged
Fitting Repeat 2 

# weights:  103
initial  value 104.495453 
final  value 94.485833 
converged
Fitting Repeat 3 

# weights:  103
initial  value 108.126301 
final  value 94.485830 
converged
Fitting Repeat 4 

# weights:  103
initial  value 93.765067 
iter  10 value 93.155492
iter  20 value 93.151684
iter  30 value 93.150998
iter  40 value 93.113857
iter  50 value 93.102043
final  value 93.101937 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.416046 
final  value 94.485920 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.491535 
iter  10 value 94.489309
iter  20 value 94.417817
iter  30 value 94.129109
iter  40 value 94.089176
final  value 94.089088 
converged
Fitting Repeat 2 

# weights:  305
initial  value 110.253365 
iter  10 value 93.359769
iter  20 value 93.115141
iter  30 value 93.114525
iter  40 value 93.114162
iter  50 value 93.113807
iter  60 value 93.112603
iter  70 value 93.111410
iter  80 value 93.110495
iter  90 value 86.143816
iter 100 value 84.984828
final  value 84.984828 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 111.397560 
iter  10 value 94.489191
iter  20 value 94.417819
iter  30 value 90.562955
iter  40 value 87.597540
iter  50 value 87.460764
final  value 87.459635 
converged
Fitting Repeat 4 

# weights:  305
initial  value 101.054039 
iter  10 value 94.488050
iter  20 value 94.333606
iter  30 value 88.602905
iter  40 value 86.626240
iter  50 value 86.547134
iter  60 value 86.543841
final  value 86.543697 
converged
Fitting Repeat 5 

# weights:  305
initial  value 120.717827 
iter  10 value 94.557068
iter  20 value 88.901335
iter  30 value 88.892392
iter  40 value 88.887520
iter  50 value 88.680545
iter  60 value 83.514774
iter  70 value 82.378586
iter  80 value 81.909588
iter  90 value 81.660614
iter 100 value 81.591771
final  value 81.591771 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 95.369682 
iter  10 value 94.475467
iter  20 value 94.467483
iter  30 value 94.274451
iter  40 value 88.044705
iter  50 value 88.025212
iter  60 value 88.018634
iter  70 value 87.789655
iter  80 value 86.058625
iter  90 value 85.746965
iter 100 value 85.743365
final  value 85.743365 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 102.510867 
iter  10 value 94.434255
iter  20 value 88.773076
iter  30 value 88.724603
iter  40 value 88.722836
iter  50 value 87.404239
iter  60 value 85.888413
iter  70 value 85.668994
iter  80 value 84.856926
iter  90 value 82.736441
iter 100 value 81.836006
final  value 81.836006 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 115.569746 
iter  10 value 94.492251
iter  20 value 93.883038
iter  30 value 93.382692
iter  40 value 86.695160
iter  50 value 86.642223
iter  60 value 86.641040
iter  70 value 86.189455
iter  80 value 85.967494
iter  90 value 85.967339
iter 100 value 85.966360
final  value 85.966360 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 102.350634 
iter  10 value 94.491544
iter  20 value 86.301844
final  value 86.055243 
converged
Fitting Repeat 5 

# weights:  507
initial  value 102.593100 
iter  10 value 93.920322
iter  20 value 93.914688
iter  30 value 86.401644
iter  40 value 85.221037
iter  50 value 85.153896
final  value 85.153412 
converged
Fitting Repeat 1 

# weights:  305
initial  value 131.298858 
iter  10 value 117.763738
iter  20 value 117.420587
iter  30 value 107.577586
iter  40 value 105.117330
iter  50 value 103.447439
iter  60 value 102.619748
iter  70 value 102.474273
iter  80 value 102.469336
iter  90 value 102.184519
iter 100 value 101.702000
final  value 101.702000 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 131.681906 
iter  10 value 117.210640
iter  20 value 108.439074
iter  30 value 103.322488
iter  40 value 103.118305
iter  50 value 103.048549
iter  60 value 102.410740
iter  70 value 101.654694
final  value 101.625245 
converged
Fitting Repeat 3 

# weights:  305
initial  value 124.857470 
iter  10 value 117.895160
iter  20 value 117.887473
final  value 117.758855 
converged
Fitting Repeat 4 

# weights:  305
initial  value 127.034569 
iter  10 value 117.899278
iter  20 value 117.896073
iter  30 value 117.615757
iter  40 value 117.519488
iter  50 value 117.511826
iter  60 value 111.339081
iter  70 value 105.056711
final  value 105.055392 
converged
Fitting Repeat 5 

# weights:  305
initial  value 120.922443 
iter  10 value 117.190128
iter  20 value 117.166821
iter  30 value 116.975813
iter  40 value 115.564135
iter  50 value 114.476158
iter  60 value 106.394772
iter  70 value 105.342269
iter  80 value 104.935916
iter  90 value 104.928943
final  value 104.928931 
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 -- Tue Jan 20 20:21:43 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 
 19.787   0.455  72.052