##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:HPiP.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings HPiP_1.17.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck’
* using R Under development (unstable) (2026-03-05 r89546)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
    GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
* running under: Ubuntu 24.04.4 LTS
* using session charset: UTF-8
* current time: 2026-03-17 04:21:16 UTC
* 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 loading without being on the library search path ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       35.544  0.639  37.145
corr_plot     35.528  0.409  35.986
FSmethod      33.479  0.569  34.050
pred_ensembel 13.089  0.301  12.092
enrichfindP    0.617  0.041  13.642
* 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 re-building of vignette outputs ... OK
* checking PDF version of manual ... OK
* DONE

Status: 2 NOTEs
See
  ‘/home/biocbuild/bbs-3.23-bioc/meat/HPiP.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD INSTALL HPiP
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.23-bioc/R/site-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) (2026-03-05 r89546) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

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 101.910905 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.776429 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.706371 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.789532 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.965215 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.754563 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 96.603254 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.549517 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.602786 
iter  10 value 93.722231
final  value 93.722222 
converged
Fitting Repeat 5 

# weights:  305
initial  value 100.144603 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 104.120654 
iter  10 value 94.008696
iter  10 value 94.008696
iter  10 value 94.008696
final  value 94.008696 
converged
Fitting Repeat 2 

# weights:  507
initial  value 108.447249 
final  value 94.008696 
converged
Fitting Repeat 3 

# weights:  507
initial  value 118.608490 
iter  10 value 93.637236
final  value 93.636784 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.789565 
final  value 94.008696 
converged
Fitting Repeat 5 

# weights:  507
initial  value 108.489700 
final  value 94.008696 
converged
Fitting Repeat 1 

# weights:  103
initial  value 100.825433 
iter  10 value 87.512374
iter  20 value 83.865688
iter  30 value 81.741432
iter  40 value 80.035874
iter  50 value 79.149465
iter  60 value 78.758088
iter  70 value 78.611342
iter  80 value 78.584538
final  value 78.584536 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.398325 
iter  10 value 94.080825
iter  20 value 94.052136
iter  30 value 94.024451
iter  40 value 93.696308
iter  50 value 83.708023
iter  60 value 82.986840
iter  70 value 82.776067
iter  80 value 82.632345
iter  90 value 82.606259
iter 100 value 82.600001
final  value 82.600001 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 102.368947 
iter  10 value 94.079514
iter  20 value 93.887961
iter  30 value 84.551732
iter  40 value 83.014557
iter  50 value 82.812339
iter  60 value 82.655407
iter  70 value 82.606842
final  value 82.600000 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.618017 
iter  10 value 91.682379
iter  20 value 81.736416
iter  30 value 79.985812
iter  40 value 79.372511
iter  50 value 79.124636
iter  60 value 79.075268
iter  70 value 79.034217
iter  80 value 78.787793
iter  90 value 78.592145
final  value 78.584536 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.124505 
iter  10 value 94.062382
iter  20 value 90.014780
iter  30 value 88.021551
iter  40 value 85.595553
iter  50 value 85.361660
iter  60 value 82.691326
iter  70 value 82.602047
final  value 82.600000 
converged
Fitting Repeat 1 

# weights:  305
initial  value 112.531055 
iter  10 value 88.549643
iter  20 value 87.066104
iter  30 value 85.779521
iter  40 value 84.134748
iter  50 value 82.389389
iter  60 value 82.057670
iter  70 value 79.883480
iter  80 value 78.877519
iter  90 value 78.415677
iter 100 value 78.179760
final  value 78.179760 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 122.196190 
iter  10 value 93.372505
iter  20 value 88.133888
iter  30 value 84.873858
iter  40 value 84.031966
iter  50 value 83.727706
iter  60 value 83.393134
iter  70 value 81.207699
iter  80 value 79.413850
iter  90 value 79.120141
iter 100 value 79.047376
final  value 79.047376 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 111.773323 
iter  10 value 94.045470
iter  20 value 91.613212
iter  30 value 84.778036
iter  40 value 81.778982
iter  50 value 79.377224
iter  60 value 78.545124
iter  70 value 77.872509
iter  80 value 77.345605
iter  90 value 77.334304
iter 100 value 77.282498
final  value 77.282498 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 109.275780 
iter  10 value 94.017980
iter  20 value 90.743539
iter  30 value 86.589279
iter  40 value 83.982682
iter  50 value 80.738795
iter  60 value 79.485527
iter  70 value 78.829976
iter  80 value 78.613542
iter  90 value 78.574114
iter 100 value 78.546479
final  value 78.546479 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.671269 
iter  10 value 94.057230
iter  20 value 94.029870
iter  30 value 93.671621
iter  40 value 93.612717
iter  50 value 87.198407
iter  60 value 80.869548
iter  70 value 79.283354
iter  80 value 78.449293
iter  90 value 78.217168
iter 100 value 77.775666
final  value 77.775666 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.769181 
iter  10 value 97.981059
iter  20 value 93.800167
iter  30 value 87.372194
iter  40 value 86.068557
iter  50 value 85.373691
iter  60 value 83.701685
iter  70 value 82.835047
iter  80 value 80.598419
iter  90 value 79.953813
iter 100 value 79.346439
final  value 79.346439 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 121.063680 
iter  10 value 95.784775
iter  20 value 94.554416
iter  30 value 89.368450
iter  40 value 83.032435
iter  50 value 80.746474
iter  60 value 79.223103
iter  70 value 78.983669
iter  80 value 78.386471
iter  90 value 77.903624
iter 100 value 77.705008
final  value 77.705008 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 136.488418 
iter  10 value 94.092430
iter  20 value 88.296090
iter  30 value 83.438886
iter  40 value 79.672040
iter  50 value 78.734793
iter  60 value 77.156931
iter  70 value 77.057775
iter  80 value 76.958150
iter  90 value 76.904128
iter 100 value 76.850604
final  value 76.850604 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 126.160684 
iter  10 value 93.922079
iter  20 value 85.768093
iter  30 value 83.669343
iter  40 value 81.725876
iter  50 value 80.853377
iter  60 value 79.301950
iter  70 value 78.634734
iter  80 value 78.516275
iter  90 value 77.950300
iter 100 value 77.702878
final  value 77.702878 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 105.154178 
iter  10 value 91.701560
iter  20 value 83.614303
iter  30 value 83.183905
iter  40 value 81.723993
iter  50 value 80.097984
iter  60 value 79.016491
iter  70 value 78.359666
iter  80 value 77.207840
iter  90 value 76.817786
iter 100 value 76.565707
final  value 76.565707 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 105.101528 
final  value 94.054640 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.151539 
iter  10 value 94.054488
final  value 94.052914 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.574541 
final  value 94.054795 
converged
Fitting Repeat 4 

# weights:  103
initial  value 96.595297 
iter  10 value 94.054566
iter  20 value 94.025347
final  value 93.603716 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.304342 
final  value 94.054772 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.845639 
iter  10 value 92.325355
iter  20 value 86.901586
iter  30 value 84.830672
iter  40 value 83.733961
iter  50 value 83.694872
iter  60 value 83.694188
iter  70 value 82.976894
final  value 82.963837 
converged
Fitting Repeat 2 

# weights:  305
initial  value 107.370530 
iter  10 value 93.695405
iter  20 value 93.687792
iter  30 value 93.602204
iter  40 value 93.554231
iter  50 value 92.957052
iter  60 value 79.994752
iter  70 value 79.553255
final  value 79.552379 
converged
Fitting Repeat 3 

# weights:  305
initial  value 110.046900 
iter  10 value 94.013346
iter  20 value 94.011728
iter  30 value 94.009025
final  value 94.008763 
converged
Fitting Repeat 4 

# weights:  305
initial  value 108.539395 
iter  10 value 94.057034
iter  20 value 93.622171
final  value 93.602408 
converged
Fitting Repeat 5 

# weights:  305
initial  value 94.543022 
iter  10 value 93.317051
iter  20 value 93.232195
iter  30 value 93.219792
iter  40 value 92.400321
iter  50 value 92.372599
final  value 92.372566 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.716521 
iter  10 value 94.017393
iter  20 value 93.353877
iter  30 value 86.258673
iter  40 value 86.171685
iter  50 value 86.167357
iter  60 value 86.166786
iter  70 value 84.079834
iter  80 value 84.077230
iter  90 value 83.989068
iter 100 value 83.988487
final  value 83.988487 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 97.231340 
iter  10 value 93.679729
iter  20 value 89.973060
iter  30 value 83.706499
iter  40 value 81.578781
iter  50 value 80.450528
iter  60 value 80.395454
iter  70 value 78.691068
iter  80 value 78.239653
iter  90 value 78.121826
iter 100 value 78.120651
final  value 78.120651 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 98.351808 
iter  10 value 94.060775
iter  20 value 94.006058
iter  30 value 86.636606
iter  40 value 85.209564
final  value 85.170761 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.444766 
iter  10 value 93.868176
iter  20 value 93.853436
iter  30 value 84.092922
iter  40 value 83.967178
iter  50 value 81.038135
iter  60 value 80.994035
iter  70 value 80.964707
iter  80 value 80.701635
iter  90 value 80.667509
iter 100 value 80.581309
final  value 80.581309 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.307822 
iter  10 value 94.060750
iter  20 value 93.330984
iter  30 value 86.086070
iter  40 value 84.239372
iter  50 value 79.462643
iter  60 value 79.428029
iter  70 value 79.427492
iter  80 value 79.426150
iter  90 value 78.248992
iter 100 value 77.422979
final  value 77.422979 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 122.917304 
iter  10 value 94.275928
final  value 94.275362 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.739432 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.825005 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.564236 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.441866 
final  value 94.275362 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.141351 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 105.692083 
final  value 94.046703 
converged
Fitting Repeat 3 

# weights:  305
initial  value 106.583045 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.317676 
final  value 94.479532 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.791526 
final  value 94.275362 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.932623 
final  value 94.484210 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.527187 
iter  10 value 94.276578
final  value 94.275362 
converged
Fitting Repeat 3 

# weights:  507
initial  value 108.045533 
iter  10 value 89.561029
iter  20 value 88.302854
iter  30 value 87.114006
iter  40 value 86.177032
iter  50 value 86.162144
iter  60 value 86.161341
final  value 86.161325 
converged
Fitting Repeat 4 

# weights:  507
initial  value 96.879493 
iter  10 value 92.900642
iter  20 value 89.663132
iter  30 value 89.344735
iter  40 value 89.307023
iter  50 value 88.930048
final  value 88.431853 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.654931 
iter  10 value 88.928077
iter  20 value 85.251157
final  value 85.249226 
converged
Fitting Repeat 1 

# weights:  103
initial  value 103.588153 
iter  10 value 94.548902
iter  20 value 93.844555
iter  30 value 87.576477
iter  40 value 86.764880
iter  50 value 84.679030
iter  60 value 84.651523
iter  70 value 81.923198
iter  80 value 81.670294
final  value 81.657223 
converged
Fitting Repeat 2 

# weights:  103
initial  value 101.403697 
iter  10 value 94.525695
iter  20 value 94.480836
iter  30 value 94.345896
iter  40 value 94.331794
iter  50 value 87.837833
iter  60 value 86.262929
iter  70 value 84.100735
iter  80 value 82.987317
iter  90 value 82.352770
iter 100 value 81.667913
final  value 81.667913 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 97.024859 
iter  10 value 94.554696
iter  20 value 86.169597
iter  30 value 85.265408
iter  40 value 84.746606
iter  50 value 84.172503
iter  60 value 83.977411
iter  70 value 81.307967
iter  80 value 81.203126
final  value 81.203082 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.235664 
iter  10 value 94.482434
iter  20 value 94.017630
iter  30 value 89.865302
iter  40 value 84.908777
iter  50 value 84.719970
final  value 84.719954 
converged
Fitting Repeat 5 

# weights:  103
initial  value 108.317182 
iter  10 value 94.496563
iter  20 value 89.274365
iter  30 value 83.617588
iter  40 value 82.722084
iter  50 value 81.763401
iter  60 value 81.657326
final  value 81.657223 
converged
Fitting Repeat 1 

# weights:  305
initial  value 119.464593 
iter  10 value 94.436561
iter  20 value 85.131495
iter  30 value 83.169242
iter  40 value 82.786356
iter  50 value 82.455265
iter  60 value 80.483067
iter  70 value 79.562204
iter  80 value 78.990785
iter  90 value 78.821327
iter 100 value 78.640891
final  value 78.640891 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.623377 
iter  10 value 94.398828
iter  20 value 86.882292
iter  30 value 83.382909
iter  40 value 83.196232
iter  50 value 82.656672
iter  60 value 82.190883
iter  70 value 79.837314
iter  80 value 79.320645
iter  90 value 79.064400
iter 100 value 78.881475
final  value 78.881475 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 105.087457 
iter  10 value 94.459386
iter  20 value 90.383580
iter  30 value 87.028951
iter  40 value 82.384228
iter  50 value 80.300831
iter  60 value 79.009898
iter  70 value 78.535223
iter  80 value 78.359255
iter  90 value 78.332835
iter 100 value 78.287306
final  value 78.287306 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 102.797229 
iter  10 value 95.045117
iter  20 value 93.807618
iter  30 value 88.491844
iter  40 value 85.862146
iter  50 value 85.179104
iter  60 value 82.386995
iter  70 value 81.911794
iter  80 value 81.283760
iter  90 value 80.237396
iter 100 value 80.099990
final  value 80.099990 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 113.997627 
iter  10 value 93.962042
iter  20 value 92.309026
iter  30 value 84.931130
iter  40 value 82.634117
iter  50 value 82.317797
iter  60 value 81.445603
iter  70 value 81.144307
iter  80 value 80.646674
iter  90 value 79.922361
iter 100 value 79.686277
final  value 79.686277 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 107.321389 
iter  10 value 97.083326
iter  20 value 94.716189
iter  30 value 93.205822
iter  40 value 91.376054
iter  50 value 81.488894
iter  60 value 80.582319
iter  70 value 79.663473
iter  80 value 79.444574
iter  90 value 79.308441
iter 100 value 79.135182
final  value 79.135182 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.514174 
iter  10 value 93.372434
iter  20 value 85.114129
iter  30 value 83.971977
iter  40 value 80.801743
iter  50 value 80.146902
iter  60 value 80.003401
iter  70 value 79.813546
iter  80 value 79.741270
iter  90 value 79.680656
iter 100 value 79.551948
final  value 79.551948 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.884476 
iter  10 value 94.415921
iter  20 value 85.636798
iter  30 value 82.841419
iter  40 value 81.578317
iter  50 value 78.747214
iter  60 value 78.513642
iter  70 value 78.429369
iter  80 value 78.385301
iter  90 value 78.319955
iter 100 value 78.172287
final  value 78.172287 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 111.913106 
iter  10 value 94.457322
iter  20 value 93.331519
iter  30 value 83.688473
iter  40 value 81.984891
iter  50 value 81.434671
iter  60 value 80.893289
iter  70 value 80.737606
iter  80 value 80.395365
iter  90 value 79.869022
iter 100 value 79.448529
final  value 79.448529 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 116.073623 
iter  10 value 94.741605
iter  20 value 87.088405
iter  30 value 84.324946
iter  40 value 81.871051
iter  50 value 81.308793
iter  60 value 80.811741
iter  70 value 79.991068
iter  80 value 79.609298
iter  90 value 79.058335
iter 100 value 78.672879
final  value 78.672879 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.755703 
final  value 94.485978 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.025455 
iter  10 value 87.977622
iter  20 value 84.215818
iter  30 value 84.158499
iter  40 value 84.155448
iter  50 value 83.163994
iter  60 value 83.048913
iter  70 value 83.048276
final  value 83.048134 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.724287 
iter  10 value 90.622828
iter  20 value 87.098556
iter  30 value 86.670871
iter  40 value 86.596754
iter  50 value 86.593975
final  value 86.593674 
converged
Fitting Repeat 4 

# weights:  103
initial  value 108.291837 
final  value 94.485861 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.690199 
iter  10 value 93.901058
iter  20 value 93.891000
iter  30 value 93.889579
final  value 93.889069 
converged
Fitting Repeat 1 

# weights:  305
initial  value 108.219279 
iter  10 value 94.490941
iter  20 value 94.476189
iter  30 value 83.583391
iter  40 value 83.375758
iter  50 value 83.304565
iter  60 value 83.271802
iter  70 value 83.266613
iter  80 value 83.067510
iter  90 value 82.931730
iter 100 value 82.929963
final  value 82.929963 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 97.192003 
iter  10 value 94.489326
final  value 94.484833 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.108954 
iter  10 value 94.487908
iter  20 value 89.117015
iter  30 value 83.892111
iter  40 value 83.648276
iter  50 value 83.470892
iter  60 value 83.138838
iter  70 value 78.758130
iter  80 value 77.993202
iter  90 value 77.448813
iter 100 value 77.363096
final  value 77.363096 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 103.053852 
iter  10 value 94.489200
iter  20 value 94.308806
iter  30 value 94.275881
iter  40 value 94.275643
iter  50 value 94.204677
iter  60 value 86.771537
iter  70 value 84.280070
iter  80 value 83.573170
iter  90 value 82.385575
iter 100 value 82.349857
final  value 82.349857 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 103.274715 
iter  10 value 94.488940
iter  20 value 94.484037
iter  30 value 93.704327
iter  40 value 85.709270
iter  50 value 83.983833
final  value 83.981620 
converged
Fitting Repeat 1 

# weights:  507
initial  value 106.153938 
iter  10 value 92.913581
iter  20 value 92.428404
iter  30 value 92.308774
iter  40 value 91.869905
iter  50 value 91.868180
iter  60 value 91.865427
iter  70 value 91.863884
iter  80 value 90.518538
iter  90 value 88.651336
iter 100 value 81.439249
final  value 81.439249 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 100.486087 
iter  10 value 94.491872
iter  20 value 94.467888
iter  30 value 87.296009
iter  40 value 82.767951
iter  50 value 82.020661
iter  60 value 82.020260
iter  70 value 81.491466
iter  80 value 80.938881
iter  90 value 80.847535
iter 100 value 80.846781
final  value 80.846781 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 96.939560 
iter  10 value 94.283709
iter  20 value 94.276052
iter  30 value 94.163561
iter  40 value 85.920151
iter  50 value 85.326776
iter  60 value 84.126343
iter  70 value 79.896119
iter  80 value 78.578855
iter  90 value 78.495545
iter 100 value 78.125582
final  value 78.125582 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 113.423425 
iter  10 value 94.492596
iter  20 value 94.483590
iter  30 value 84.517411
iter  40 value 84.148214
iter  50 value 80.249287
iter  60 value 79.225141
iter  70 value 79.144584
iter  80 value 79.125162
iter  90 value 78.948218
iter 100 value 77.737711
final  value 77.737711 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 106.133914 
iter  10 value 94.297595
iter  20 value 94.288666
iter  30 value 93.884764
iter  40 value 84.889771
iter  50 value 81.352338
iter  60 value 78.128367
iter  70 value 77.728970
iter  80 value 77.456119
iter  90 value 77.451405
iter 100 value 77.443415
final  value 77.443415 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 109.285037 
final  value 93.836066 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.687740 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 94.743387 
final  value 93.836066 
converged
Fitting Repeat 4 

# weights:  103
initial  value 99.023968 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.048118 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.476170 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.720161 
final  value 93.836066 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.143944 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  305
initial  value 94.246370 
iter  10 value 90.643623
iter  20 value 87.201793
iter  30 value 86.804659
final  value 86.804245 
converged
Fitting Repeat 5 

# weights:  305
initial  value 97.214153 
final  value 93.288889 
converged
Fitting Repeat 1 

# weights:  507
initial  value 99.839982 
iter  10 value 93.576020
iter  20 value 93.327669
iter  30 value 93.326350
iter  30 value 93.326350
iter  30 value 93.326350
final  value 93.326350 
converged
Fitting Repeat 2 

# weights:  507
initial  value 105.562748 
iter  10 value 93.604826
final  value 93.604520 
converged
Fitting Repeat 3 

# weights:  507
initial  value 99.924440 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.029598 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 107.034308 
iter  10 value 89.897848
iter  20 value 88.480172
final  value 88.469069 
converged
Fitting Repeat 1 

# weights:  103
initial  value 97.704764 
iter  10 value 94.034610
iter  20 value 93.352318
iter  30 value 86.111489
iter  40 value 85.625880
iter  50 value 85.459995
iter  60 value 85.230338
iter  70 value 84.947648
iter  80 value 84.674617
iter  90 value 84.637256
iter 100 value 84.597361
final  value 84.597361 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 101.431535 
iter  10 value 94.089704
iter  20 value 93.416316
iter  30 value 93.378943
iter  40 value 91.224371
iter  50 value 89.688821
iter  60 value 89.153428
iter  70 value 88.093107
iter  80 value 87.573861
iter  90 value 87.467673
iter 100 value 87.463943
final  value 87.463943 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 98.839166 
iter  10 value 94.045471
iter  20 value 93.444056
iter  30 value 93.389437
iter  40 value 93.375609
iter  50 value 88.207463
iter  60 value 87.595790
iter  70 value 86.198688
iter  80 value 85.941571
iter  90 value 85.818243
iter 100 value 85.600051
final  value 85.600051 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.840770 
iter  10 value 94.056816
iter  20 value 93.898469
iter  30 value 93.543769
iter  40 value 93.469742
iter  50 value 93.468592
iter  60 value 93.467659
iter  70 value 93.270371
iter  80 value 88.446870
iter  90 value 88.021377
iter 100 value 87.763724
final  value 87.763724 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 107.220045 
iter  10 value 94.496119
iter  20 value 94.042705
iter  30 value 86.264629
iter  40 value 85.987567
iter  50 value 85.655519
iter  60 value 85.215324
iter  70 value 84.717353
iter  80 value 84.571412
iter  90 value 84.543111
final  value 84.535392 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.966042 
iter  10 value 93.145099
iter  20 value 88.287316
iter  30 value 87.609590
iter  40 value 86.597037
iter  50 value 86.347815
iter  60 value 85.991180
iter  70 value 85.480453
iter  80 value 84.872982
iter  90 value 84.059168
iter 100 value 83.809619
final  value 83.809619 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.855730 
iter  10 value 93.917375
iter  20 value 92.456100
iter  30 value 92.176165
iter  40 value 91.774610
iter  50 value 88.962179
iter  60 value 87.617569
iter  70 value 87.398107
iter  80 value 86.436843
iter  90 value 85.953543
iter 100 value 85.781577
final  value 85.781577 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.110904 
iter  10 value 93.708063
iter  20 value 90.044374
iter  30 value 87.740571
iter  40 value 86.857386
iter  50 value 86.673848
iter  60 value 86.563607
iter  70 value 85.988065
iter  80 value 84.930454
iter  90 value 83.992505
iter 100 value 83.628320
final  value 83.628320 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 98.995836 
iter  10 value 93.832810
iter  20 value 89.242495
iter  30 value 87.132706
iter  40 value 87.027339
iter  50 value 86.847613
iter  60 value 86.696130
iter  70 value 85.728468
iter  80 value 84.727497
iter  90 value 84.051716
iter 100 value 83.911736
final  value 83.911736 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.354978 
iter  10 value 93.505923
iter  20 value 93.166313
iter  30 value 92.113197
iter  40 value 88.566912
iter  50 value 87.531381
iter  60 value 87.300605
iter  70 value 86.983307
iter  80 value 86.470747
iter  90 value 85.543256
iter 100 value 84.787920
final  value 84.787920 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 129.390093 
iter  10 value 92.977887
iter  20 value 89.433099
iter  30 value 88.598740
iter  40 value 87.605961
iter  50 value 86.738703
iter  60 value 85.831680
iter  70 value 85.162928
iter  80 value 84.151015
iter  90 value 83.878060
iter 100 value 83.706423
final  value 83.706423 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 122.922375 
iter  10 value 94.085301
iter  20 value 94.054140
iter  30 value 86.264128
iter  40 value 85.519756
iter  50 value 84.904958
iter  60 value 84.596820
iter  70 value 84.562310
iter  80 value 84.480342
iter  90 value 83.954013
iter 100 value 83.608502
final  value 83.608502 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 104.356019 
iter  10 value 94.112359
iter  20 value 92.645264
iter  30 value 88.876301
iter  40 value 87.698988
iter  50 value 86.306866
iter  60 value 85.275473
iter  70 value 84.819969
iter  80 value 84.227638
iter  90 value 84.028830
iter 100 value 83.732745
final  value 83.732745 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.089631 
iter  10 value 91.868850
iter  20 value 89.568987
iter  30 value 88.148818
iter  40 value 87.314074
iter  50 value 86.556628
iter  60 value 86.224801
iter  70 value 85.762951
iter  80 value 85.598387
iter  90 value 85.484660
iter 100 value 85.421303
final  value 85.421303 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 133.736304 
iter  10 value 93.937942
iter  20 value 89.755488
iter  30 value 88.015834
iter  40 value 87.937646
iter  50 value 87.699587
iter  60 value 85.627875
iter  70 value 84.450914
iter  80 value 83.844345
iter  90 value 83.474314
iter 100 value 83.204039
final  value 83.204039 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.015660 
final  value 94.054574 
converged
Fitting Repeat 2 

# weights:  103
initial  value 94.406733 
final  value 94.054831 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.208255 
final  value 94.054685 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.161747 
final  value 94.054690 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.790092 
final  value 93.837542 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.086785 
iter  10 value 94.057698
iter  20 value 93.893100
iter  30 value 88.120827
iter  40 value 88.000961
iter  50 value 87.992456
iter  60 value 87.977272
iter  70 value 87.887144
iter  80 value 87.859454
iter  90 value 87.796287
iter 100 value 87.789807
final  value 87.789807 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 102.841946 
iter  10 value 94.051823
iter  20 value 93.327171
iter  30 value 93.270480
iter  40 value 91.984769
iter  50 value 91.977441
iter  60 value 89.271844
iter  70 value 88.319371
iter  80 value 87.447282
iter  90 value 87.138328
iter 100 value 85.704950
final  value 85.704950 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 97.123037 
iter  10 value 93.841369
iter  20 value 93.784616
iter  30 value 87.321327
final  value 87.209797 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.543118 
iter  10 value 92.491407
iter  20 value 92.459355
iter  30 value 92.459033
iter  40 value 92.455448
iter  50 value 89.256392
iter  60 value 85.745257
iter  70 value 84.746920
iter  80 value 84.651309
iter  90 value 84.602302
iter 100 value 84.268766
final  value 84.268766 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 98.465694 
iter  10 value 93.340324
iter  20 value 93.326955
iter  30 value 92.133899
iter  40 value 87.610140
iter  50 value 85.026336
iter  60 value 84.395739
iter  70 value 84.380185
iter  80 value 84.358661
iter  90 value 84.293767
iter 100 value 84.020964
final  value 84.020964 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 101.703126 
iter  10 value 94.061622
iter  20 value 93.634321
iter  30 value 93.319340
iter  40 value 93.317836
iter  50 value 91.152885
iter  60 value 87.507718
iter  70 value 87.485684
iter  80 value 87.438779
iter  90 value 87.155923
final  value 87.155759 
converged
Fitting Repeat 2 

# weights:  507
initial  value 100.810492 
iter  10 value 93.331771
iter  20 value 93.322502
iter  30 value 93.258752
iter  40 value 93.257971
final  value 93.257849 
converged
Fitting Repeat 3 

# weights:  507
initial  value 93.423601 
iter  10 value 90.283955
iter  20 value 90.283025
iter  30 value 90.202716
iter  40 value 90.194373
final  value 90.193484 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.544990 
iter  10 value 92.324748
iter  20 value 90.825051
iter  30 value 90.820277
iter  40 value 88.672554
iter  50 value 87.751138
iter  60 value 87.747246
iter  70 value 87.744773
iter  80 value 87.477655
iter  90 value 87.220251
final  value 87.220214 
converged
Fitting Repeat 5 

# weights:  507
initial  value 97.340184 
iter  10 value 94.060321
iter  20 value 93.591820
iter  30 value 87.470179
iter  40 value 85.941906
iter  50 value 84.102612
iter  60 value 83.331853
iter  70 value 83.125566
iter  80 value 83.116051
iter  90 value 83.114884
iter 100 value 83.050710
final  value 83.050710 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 95.515671 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.540101 
final  value 94.466823 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.815566 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.154669 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 107.478134 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 101.879549 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 109.045034 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 94.060257 
iter  10 value 86.307985
final  value 86.307887 
converged
Fitting Repeat 4 

# weights:  305
initial  value 98.344892 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.147068 
final  value 94.466823 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.829546 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.858663 
iter  10 value 94.139558
iter  20 value 94.139380
final  value 94.139368 
converged
Fitting Repeat 3 

# weights:  507
initial  value 114.072779 
iter  10 value 92.258857
iter  20 value 90.551114
iter  30 value 90.547767
final  value 90.547764 
converged
Fitting Repeat 4 

# weights:  507
initial  value 123.189547 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 96.735611 
iter  10 value 94.389914
final  value 94.389548 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.283089 
iter  10 value 94.488561
iter  20 value 93.015800
iter  30 value 85.043435
iter  40 value 84.529638
iter  50 value 84.448222
iter  60 value 82.764305
iter  70 value 81.921392
iter  80 value 81.652293
iter  90 value 81.617668
iter 100 value 81.592839
final  value 81.592839 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 97.029371 
iter  10 value 94.489484
iter  20 value 86.263068
iter  30 value 83.145756
iter  40 value 82.528576
iter  50 value 82.177003
iter  60 value 81.834431
iter  70 value 81.610101
iter  80 value 81.592102
iter  80 value 81.592102
iter  80 value 81.592102
final  value 81.592102 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.158373 
iter  10 value 94.481105
iter  20 value 91.517947
iter  30 value 88.296961
iter  40 value 86.166677
iter  50 value 79.684315
iter  60 value 79.310587
iter  70 value 79.099412
iter  80 value 78.947976
final  value 78.944423 
converged
Fitting Repeat 4 

# weights:  103
initial  value 100.729281 
iter  10 value 93.661303
iter  20 value 86.052848
iter  30 value 83.147222
iter  40 value 82.320458
iter  50 value 82.049545
iter  60 value 81.988336
iter  70 value 80.346097
iter  80 value 79.549675
iter  90 value 79.542448
iter 100 value 79.400604
final  value 79.400604 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 100.389981 
iter  10 value 94.453166
iter  20 value 92.604220
iter  30 value 90.341063
iter  40 value 90.252780
iter  50 value 83.247230
iter  60 value 82.160254
iter  70 value 81.862178
iter  80 value 81.719655
iter  90 value 81.478025
iter 100 value 81.285893
final  value 81.285893 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 115.101592 
iter  10 value 94.259632
iter  20 value 87.383460
iter  30 value 84.290645
iter  40 value 81.159766
iter  50 value 79.483079
iter  60 value 78.581771
iter  70 value 78.016934
iter  80 value 77.969016
iter  90 value 77.966584
iter 100 value 77.966433
final  value 77.966433 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 106.212756 
iter  10 value 94.564164
iter  20 value 87.460352
iter  30 value 85.291374
iter  40 value 83.046971
iter  50 value 82.531257
iter  60 value 82.346426
iter  70 value 81.304185
iter  80 value 79.696104
iter  90 value 78.715420
iter 100 value 78.158491
final  value 78.158491 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.111682 
iter  10 value 94.486852
iter  20 value 94.247252
iter  30 value 86.450397
iter  40 value 85.502203
iter  50 value 84.807947
iter  60 value 82.864319
iter  70 value 81.427345
iter  80 value 79.261071
iter  90 value 79.053689
iter 100 value 78.690446
final  value 78.690446 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 99.900477 
iter  10 value 94.539362
iter  20 value 92.686932
iter  30 value 87.337403
iter  40 value 87.074308
iter  50 value 86.146023
iter  60 value 83.009218
iter  70 value 80.620665
iter  80 value 78.940194
iter  90 value 78.422169
iter 100 value 78.128505
final  value 78.128505 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.829364 
iter  10 value 95.831455
iter  20 value 94.242076
iter  30 value 87.207443
iter  40 value 86.681265
iter  50 value 86.278486
iter  60 value 81.771878
iter  70 value 79.058862
iter  80 value 78.643790
iter  90 value 78.261453
iter 100 value 77.877969
final  value 77.877969 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 101.738836 
iter  10 value 93.947561
iter  20 value 85.347116
iter  30 value 84.241081
iter  40 value 83.437324
iter  50 value 81.266009
iter  60 value 81.018446
iter  70 value 80.607653
iter  80 value 79.244928
iter  90 value 78.811685
iter 100 value 78.626110
final  value 78.626110 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 106.587319 
iter  10 value 94.675849
iter  20 value 93.987367
iter  30 value 88.372934
iter  40 value 84.014671
iter  50 value 83.289351
iter  60 value 81.225180
iter  70 value 79.797116
iter  80 value 79.345218
iter  90 value 79.147746
iter 100 value 78.884940
final  value 78.884940 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 113.970963 
iter  10 value 99.835888
iter  20 value 90.218618
iter  30 value 84.495380
iter  40 value 82.815241
iter  50 value 82.034606
iter  60 value 81.793810
iter  70 value 81.640435
iter  80 value 80.995088
iter  90 value 79.287802
iter 100 value 78.583271
final  value 78.583271 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 107.183715 
iter  10 value 93.495021
iter  20 value 86.508423
iter  30 value 84.380949
iter  40 value 81.060534
iter  50 value 80.591414
iter  60 value 80.310832
iter  70 value 80.254289
iter  80 value 80.198285
iter  90 value 79.896615
iter 100 value 79.060073
final  value 79.060073 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 103.919488 
iter  10 value 93.781194
iter  20 value 87.916787
iter  30 value 84.249662
iter  40 value 83.330247
iter  50 value 81.179684
iter  60 value 80.071898
iter  70 value 79.659602
iter  80 value 79.444340
iter  90 value 79.004671
iter 100 value 78.986076
final  value 78.986076 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.852690 
final  value 94.485806 
converged
Fitting Repeat 2 

# weights:  103
initial  value 98.738239 
final  value 94.485557 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.749019 
final  value 94.486077 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.344803 
iter  10 value 94.485860
iter  20 value 94.484214
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.540155 
final  value 94.485835 
converged
Fitting Repeat 1 

# weights:  305
initial  value 98.484097 
iter  10 value 94.486429
iter  20 value 94.076673
iter  30 value 90.856858
iter  40 value 90.746558
iter  50 value 90.746383
iter  60 value 90.050887
iter  70 value 90.019291
final  value 90.019097 
converged
Fitting Repeat 2 

# weights:  305
initial  value 95.362251 
iter  10 value 94.471795
iter  20 value 94.467231
iter  30 value 92.950821
iter  40 value 91.202115
iter  50 value 89.902267
iter  60 value 84.180885
iter  70 value 81.232692
iter  80 value 79.783849
iter  90 value 79.777794
iter 100 value 79.777488
final  value 79.777488 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 104.341209 
iter  10 value 94.488396
iter  20 value 94.450631
iter  30 value 85.565048
iter  40 value 81.476099
iter  50 value 81.265076
final  value 81.260971 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.230132 
iter  10 value 94.471771
iter  20 value 94.468377
final  value 94.467350 
converged
Fitting Repeat 5 

# weights:  305
initial  value 118.117974 
iter  10 value 94.488854
iter  20 value 94.484237
final  value 94.484225 
converged
Fitting Repeat 1 

# weights:  507
initial  value 104.913357 
iter  10 value 94.492364
iter  20 value 94.445381
iter  30 value 94.443481
iter  30 value 94.443480
iter  30 value 94.443480
final  value 94.443480 
converged
Fitting Repeat 2 

# weights:  507
initial  value 107.565760 
iter  10 value 94.492929
iter  20 value 94.449321
iter  30 value 85.172878
iter  40 value 81.032386
iter  50 value 79.062604
iter  60 value 77.989336
iter  70 value 77.328464
iter  80 value 77.113702
iter  90 value 76.873182
iter 100 value 76.831361
final  value 76.831361 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 117.292099 
iter  10 value 94.475213
iter  20 value 94.464514
iter  30 value 90.629513
final  value 90.549340 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.703498 
iter  10 value 94.493479
iter  20 value 94.484716
iter  30 value 93.941489
iter  40 value 91.826021
iter  50 value 83.637912
iter  60 value 83.240211
iter  70 value 82.190137
iter  80 value 81.897281
iter  90 value 81.271816
iter 100 value 81.264775
final  value 81.264775 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 109.990376 
iter  10 value 93.756183
iter  20 value 93.642094
iter  30 value 93.611763
iter  40 value 93.608844
iter  50 value 93.606771
iter  60 value 93.605179
final  value 93.605112 
converged
Fitting Repeat 1 

# weights:  103
initial  value 94.836174 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.770346 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.290773 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.704230 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  103
initial  value 101.473603 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.175001 
final  value 94.312038 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.235775 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 97.922525 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.270253 
iter  10 value 90.098082
iter  20 value 86.107581
iter  30 value 85.145963
final  value 85.101732 
converged
Fitting Repeat 5 

# weights:  305
initial  value 99.270447 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 95.007228 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.621683 
iter  10 value 93.936783
final  value 93.936782 
converged
Fitting Repeat 3 

# weights:  507
initial  value 105.254321 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 139.629415 
iter  10 value 94.354396
iter  10 value 94.354396
iter  10 value 94.354396
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  507
initial  value 119.635290 
iter  10 value 94.203838
final  value 94.203552 
converged
Fitting Repeat 1 

# weights:  103
initial  value 99.804428 
iter  10 value 94.487549
iter  20 value 94.381128
iter  30 value 90.258581
iter  40 value 88.210897
iter  50 value 87.256406
iter  60 value 86.310573
iter  70 value 85.393507
iter  80 value 85.152378
iter  90 value 84.898605
iter 100 value 84.847432
final  value 84.847432 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 97.738476 
iter  10 value 94.489098
iter  20 value 91.654301
iter  30 value 86.299106
iter  40 value 85.951390
iter  50 value 85.915019
iter  60 value 85.913033
iter  60 value 85.913033
iter  60 value 85.913033
final  value 85.913033 
converged
Fitting Repeat 3 

# weights:  103
initial  value 97.315262 
iter  10 value 94.548413
iter  20 value 94.321194
iter  30 value 89.951989
iter  40 value 89.148144
iter  50 value 88.332752
iter  60 value 87.844220
iter  70 value 87.710283
iter  80 value 85.369163
iter  90 value 84.858465
iter 100 value 84.499903
final  value 84.499903 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 97.942714 
iter  10 value 94.496064
iter  20 value 94.322759
iter  30 value 93.330715
iter  40 value 88.626414
iter  50 value 86.256360
iter  60 value 86.165144
iter  70 value 86.163101
final  value 86.163043 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.926318 
iter  10 value 94.370614
iter  20 value 90.970988
iter  30 value 89.179343
iter  40 value 87.025344
iter  50 value 86.224504
iter  60 value 86.163223
final  value 86.163176 
converged
Fitting Repeat 1 

# weights:  305
initial  value 120.829514 
iter  10 value 94.320341
iter  20 value 87.132465
iter  30 value 86.369169
iter  40 value 85.664438
iter  50 value 85.588264
iter  60 value 85.392411
iter  70 value 84.880405
iter  80 value 83.867691
iter  90 value 83.726476
iter 100 value 83.657243
final  value 83.657243 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.277429 
iter  10 value 94.339221
iter  20 value 88.847825
iter  30 value 86.712673
iter  40 value 86.097852
iter  50 value 86.001228
iter  60 value 85.878650
iter  70 value 85.420103
iter  80 value 84.652154
iter  90 value 84.352603
iter 100 value 84.327755
final  value 84.327755 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 107.410405 
iter  10 value 95.529582
iter  20 value 90.829979
iter  30 value 89.066036
iter  40 value 86.510957
iter  50 value 85.942938
iter  60 value 85.914551
iter  70 value 85.731048
iter  80 value 84.982564
iter  90 value 84.587654
iter 100 value 84.426540
final  value 84.426540 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.112990 
iter  10 value 96.270402
iter  20 value 94.315569
iter  30 value 86.860705
iter  40 value 86.549753
iter  50 value 86.141954
iter  60 value 85.321523
iter  70 value 84.719584
iter  80 value 84.360007
iter  90 value 83.568695
iter 100 value 83.437308
final  value 83.437308 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 104.613188 
iter  10 value 94.471860
iter  20 value 94.293593
iter  30 value 92.125441
iter  40 value 88.798602
iter  50 value 88.742401
iter  60 value 87.939936
iter  70 value 84.672800
iter  80 value 84.018219
iter  90 value 83.761550
iter 100 value 83.630855
final  value 83.630855 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 131.637354 
iter  10 value 94.529002
iter  20 value 94.117452
iter  30 value 94.089991
iter  40 value 94.019482
iter  50 value 93.405841
iter  60 value 90.961964
iter  70 value 86.440618
iter  80 value 85.360321
iter  90 value 84.895443
iter 100 value 84.214914
final  value 84.214914 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.582330 
iter  10 value 94.560142
iter  20 value 91.867051
iter  30 value 91.129413
iter  40 value 87.035100
iter  50 value 85.187610
iter  60 value 83.316655
iter  70 value 83.029077
iter  80 value 82.771448
iter  90 value 82.749256
iter 100 value 82.737242
final  value 82.737242 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 126.953882 
iter  10 value 94.627345
iter  20 value 94.217075
iter  30 value 87.536876
iter  40 value 85.841997
iter  50 value 85.682972
iter  60 value 84.502665
iter  70 value 83.764096
iter  80 value 83.556423
iter  90 value 83.304727
iter 100 value 83.007081
final  value 83.007081 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 114.880112 
iter  10 value 94.955128
iter  20 value 91.461999
iter  30 value 87.719472
iter  40 value 86.330136
iter  50 value 85.189667
iter  60 value 84.946526
iter  70 value 83.765790
iter  80 value 83.212157
iter  90 value 83.054052
iter 100 value 82.856738
final  value 82.856738 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 110.737227 
iter  10 value 94.170146
iter  20 value 86.768789
iter  30 value 86.230254
iter  40 value 85.978527
iter  50 value 85.099354
iter  60 value 84.041472
iter  70 value 83.349424
iter  80 value 83.100193
iter  90 value 82.897107
iter 100 value 82.833122
final  value 82.833122 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 124.215614 
iter  10 value 94.310583
final  value 94.309725 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.212193 
iter  10 value 94.485919
iter  20 value 94.484254
iter  30 value 94.355304
final  value 94.354514 
converged
Fitting Repeat 3 

# weights:  103
initial  value 99.071520 
final  value 94.485901 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.532890 
final  value 94.485813 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.019624 
iter  10 value 94.090565
iter  20 value 93.696619
iter  30 value 86.206853
iter  40 value 86.039841
iter  50 value 86.006391
iter  60 value 85.989845
iter  70 value 84.963188
iter  80 value 84.553914
iter  90 value 83.796184
iter 100 value 83.764308
final  value 83.764308 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 95.539580 
iter  10 value 92.337774
iter  20 value 89.087803
iter  30 value 88.821443
iter  40 value 88.815262
iter  50 value 88.813122
iter  60 value 88.228463
iter  70 value 87.562957
iter  80 value 87.187598
iter  90 value 86.196672
iter 100 value 85.885067
final  value 85.885067 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 121.113697 
iter  10 value 94.228598
iter  20 value 94.109978
iter  30 value 92.718448
iter  40 value 88.342360
iter  50 value 88.327826
iter  60 value 87.818233
iter  70 value 85.086711
iter  80 value 82.194212
iter  90 value 81.805996
iter 100 value 81.778279
final  value 81.778279 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.080480 
iter  10 value 94.488958
iter  20 value 94.330465
iter  30 value 89.258313
iter  40 value 86.157549
iter  50 value 86.152035
final  value 86.151909 
converged
Fitting Repeat 4 

# weights:  305
initial  value 97.425807 
iter  10 value 94.489165
iter  20 value 94.354473
final  value 94.354460 
converged
Fitting Repeat 5 

# weights:  305
initial  value 98.588552 
iter  10 value 94.488815
iter  20 value 94.484294
final  value 94.484284 
converged
Fitting Repeat 1 

# weights:  507
initial  value 139.333198 
iter  10 value 94.362383
iter  20 value 94.356052
iter  30 value 93.985545
iter  40 value 92.185241
iter  50 value 86.070227
iter  60 value 84.527214
iter  70 value 84.404875
iter  80 value 84.362366
iter  90 value 84.354427
iter 100 value 84.301730
final  value 84.301730 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.181179 
iter  10 value 94.492140
iter  20 value 94.285576
iter  30 value 88.774233
iter  40 value 88.603475
iter  50 value 86.839826
iter  60 value 86.282237
iter  70 value 86.232602
iter  80 value 86.232244
final  value 86.231832 
converged
Fitting Repeat 3 

# weights:  507
initial  value 101.059441 
iter  10 value 94.362730
iter  20 value 94.359147
iter  30 value 94.318312
iter  40 value 94.000031
iter  50 value 93.998523
final  value 93.998449 
converged
Fitting Repeat 4 

# weights:  507
initial  value 94.715472 
iter  10 value 94.492506
iter  20 value 94.434945
iter  30 value 94.057384
iter  40 value 94.057283
iter  50 value 88.550322
final  value 88.101806 
converged
Fitting Repeat 5 

# weights:  507
initial  value 129.986618 
iter  10 value 86.495576
iter  20 value 86.410159
iter  30 value 85.296854
iter  40 value 84.277261
iter  50 value 84.275720
iter  60 value 84.239845
iter  70 value 84.142692
iter  80 value 83.610663
iter  90 value 82.581378
iter 100 value 82.068766
final  value 82.068766 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 125.826736 
iter  10 value 116.896978
iter  20 value 112.030092
iter  30 value 107.165021
iter  40 value 105.241315
iter  50 value 104.033337
iter  60 value 101.733915
iter  70 value 101.569242
iter  80 value 101.491894
iter  90 value 101.257918
iter 100 value 100.847024
final  value 100.847024 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 125.881654 
iter  10 value 116.904404
iter  20 value 107.640658
iter  30 value 106.514085
iter  40 value 105.456534
iter  50 value 105.198124
iter  60 value 104.972456
iter  70 value 103.189344
iter  80 value 102.813781
iter  90 value 102.601893
iter 100 value 102.088797
final  value 102.088797 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 125.048801 
iter  10 value 117.784603
iter  20 value 110.869981
iter  30 value 109.457740
iter  40 value 108.402274
iter  50 value 108.025344
iter  60 value 104.655814
iter  70 value 102.663324
iter  80 value 101.982999
iter  90 value 101.551619
iter 100 value 101.385195
final  value 101.385195 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 128.742926 
iter  10 value 116.467583
iter  20 value 115.335023
iter  30 value 114.890459
iter  40 value 107.593653
iter  50 value 107.284129
iter  60 value 106.792278
iter  70 value 103.837308
iter  80 value 102.529331
iter  90 value 101.965780
iter 100 value 101.747916
final  value 101.747916 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 123.922480 
iter  10 value 118.031767
iter  20 value 117.145837
iter  30 value 112.838882
iter  40 value 111.309797
iter  50 value 110.784426
iter  60 value 109.205020
iter  70 value 103.853029
iter  80 value 103.042285
iter  90 value 101.698357
iter 100 value 101.461880
final  value 101.461880 
stopped after 100 iterations
svmRadial
ranger
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls < cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases
Setting levels: control = Positive, case = Negative
Setting direction: controls > cases


RUNIT TEST PROTOCOL -- Tue Mar 17 00:26:41 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 
 41.730   1.131  92.682