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


* using log directory ‘/Users/biocbuild/bbs-3.22-bioc/meat/HPiP.Rcheck’
* using R version 4.5.1 (2025-06-13)
* using platform: x86_64-apple-darwin20
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Monterey 12.7.6
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘HPiP/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘HPiP’ version ‘1.15.0’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘HPiP’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License stub is invalid DCF.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
checkRd: (-1) getHPI.Rd:29: Lost braces
    29 | then the Kronecker product is the code{(pm × qn)} block matrix
       |                                       ^
* checking Rd metadata ... OK
* checking Rd cross-references ... INFO
Package unavailable to check Rd xrefs: ‘ftrCOOL’
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking R/sysdata.rda ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
                user system elapsed
var_imp       35.306  1.808  37.496
FSmethod      33.253  1.670  35.157
corr_plot     33.151  1.714  35.122
pred_ensembel 13.602  0.432  12.082
enrichfindP    0.453  0.054   8.068
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘runTests.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

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

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


* installing to library ‘/Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/library’
* installing *source* package ‘HPiP’ ...
** this is package ‘HPiP’ version ‘1.15.0’
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HPiP)

R version 4.5.1 (2025-06-13) -- "Great Square Root"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20

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

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

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

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

# weights:  103
initial  value 100.956672 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 95.661922 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.949233 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.438979 
final  value 93.922222 
converged
Fitting Repeat 5 

# weights:  103
initial  value 99.797575 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.270946 
final  value 92.613874 
converged
Fitting Repeat 2 

# weights:  305
initial  value 112.809737 
iter  10 value 89.203213
iter  20 value 86.279096
final  value 86.278694 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.684312 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  305
initial  value 96.633217 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  305
initial  value 101.083984 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 94.953193 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 95.096199 
iter  10 value 89.518864
iter  20 value 89.330868
iter  30 value 89.330619
final  value 89.330616 
converged
Fitting Repeat 3 

# weights:  507
initial  value 106.770770 
final  value 94.354396 
converged
Fitting Repeat 4 

# weights:  507
initial  value 120.944596 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 101.253457 
iter  10 value 92.525868
final  value 92.525851 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.687940 
iter  10 value 93.439492
iter  20 value 87.621515
iter  30 value 86.679160
iter  40 value 86.023638
iter  50 value 85.977155
final  value 85.976823 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.240076 
iter  10 value 94.377680
iter  20 value 90.372770
iter  30 value 89.810028
iter  40 value 89.675880
iter  50 value 86.240270
iter  60 value 85.818023
iter  70 value 84.836544
iter  80 value 83.850835
iter  90 value 83.683044
iter 100 value 83.552616
final  value 83.552616 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 107.701919 
iter  10 value 92.308304
iter  20 value 87.805359
final  value 87.376045 
converged
Fitting Repeat 4 

# weights:  103
initial  value 98.041616 
iter  10 value 94.486588
iter  20 value 87.700368
iter  30 value 87.295060
iter  40 value 87.084434
iter  50 value 86.844871
iter  60 value 85.756800
iter  70 value 85.627144
final  value 85.624646 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.594866 
iter  10 value 89.540724
iter  20 value 86.225200
iter  30 value 86.139214
iter  40 value 85.865484
iter  50 value 85.628997
final  value 85.624646 
converged
Fitting Repeat 1 

# weights:  305
initial  value 109.345816 
iter  10 value 94.545000
iter  20 value 88.884444
iter  30 value 87.231519
iter  40 value 87.145062
iter  50 value 85.075381
iter  60 value 83.590336
iter  70 value 82.827886
iter  80 value 82.525058
iter  90 value 82.484983
iter 100 value 82.466725
final  value 82.466725 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 107.875460 
iter  10 value 94.946858
iter  20 value 93.622995
iter  30 value 88.972934
iter  40 value 85.285270
iter  50 value 83.394734
iter  60 value 83.177530
iter  70 value 82.988000
iter  80 value 82.843405
iter  90 value 82.601667
iter 100 value 82.597781
final  value 82.597781 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 103.964537 
iter  10 value 94.276757
iter  20 value 91.053560
iter  30 value 87.559893
iter  40 value 87.142677
iter  50 value 87.030581
iter  60 value 86.880226
iter  70 value 86.770980
iter  80 value 86.630458
iter  90 value 86.440065
iter 100 value 85.269867
final  value 85.269867 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 117.077262 
iter  10 value 94.257516
iter  20 value 92.731201
iter  30 value 92.637609
iter  40 value 88.545641
iter  50 value 87.374644
iter  60 value 85.561468
iter  70 value 84.920014
iter  80 value 84.406175
iter  90 value 84.257405
iter 100 value 83.352802
final  value 83.352802 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 118.342818 
iter  10 value 94.193523
iter  20 value 86.716099
iter  30 value 85.965867
iter  40 value 85.842837
iter  50 value 85.787994
iter  60 value 85.321332
iter  70 value 84.306703
iter  80 value 83.790069
iter  90 value 83.667254
iter 100 value 83.627425
final  value 83.627425 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 117.723959 
iter  10 value 98.985657
iter  20 value 89.366581
iter  30 value 87.371002
iter  40 value 85.481912
iter  50 value 84.094186
iter  60 value 84.004737
iter  70 value 83.693928
iter  80 value 83.202342
iter  90 value 82.880236
iter 100 value 82.564323
final  value 82.564323 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 109.980960 
iter  10 value 99.470151
iter  20 value 88.962919
iter  30 value 85.104603
iter  40 value 84.481548
iter  50 value 84.190796
iter  60 value 84.036944
iter  70 value 83.950506
iter  80 value 83.580897
iter  90 value 83.008293
iter 100 value 82.871067
final  value 82.871067 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.403676 
iter  10 value 94.293352
iter  20 value 91.045931
iter  30 value 86.791792
iter  40 value 84.627858
iter  50 value 83.390899
iter  60 value 83.220686
iter  70 value 82.738612
iter  80 value 82.470009
iter  90 value 82.223061
iter 100 value 82.124950
final  value 82.124950 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 105.707185 
iter  10 value 94.389046
iter  20 value 90.354353
iter  30 value 88.634570
iter  40 value 85.066456
iter  50 value 83.447337
iter  60 value 83.082286
iter  70 value 82.874845
iter  80 value 82.786509
iter  90 value 82.595139
iter 100 value 82.507154
final  value 82.507154 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.215693 
iter  10 value 93.896082
iter  20 value 87.616669
iter  30 value 86.803842
iter  40 value 86.076587
iter  50 value 84.345005
iter  60 value 83.261653
iter  70 value 82.926724
iter  80 value 82.773719
iter  90 value 82.544394
iter 100 value 82.260719
final  value 82.260719 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 98.101257 
final  value 94.485992 
converged
Fitting Repeat 2 

# weights:  103
initial  value 102.681319 
final  value 94.485828 
converged
Fitting Repeat 3 

# weights:  103
initial  value 100.011407 
iter  10 value 94.486050
final  value 94.484215 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.247342 
final  value 94.485817 
converged
Fitting Repeat 5 

# weights:  103
initial  value 96.320388 
iter  10 value 94.485823
iter  20 value 94.283402
iter  30 value 93.854139
iter  40 value 93.818410
iter  50 value 93.817908
iter  60 value 93.816297
iter  70 value 93.678469
iter  80 value 93.559632
iter  90 value 93.558829
final  value 93.558813 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.207772 
iter  10 value 94.488906
iter  20 value 94.483896
iter  30 value 86.892447
iter  40 value 86.751074
iter  50 value 85.640853
iter  60 value 85.638285
iter  70 value 85.573263
iter  80 value 85.555289
iter  90 value 85.552836
final  value 85.552176 
converged
Fitting Repeat 2 

# weights:  305
initial  value 106.313486 
iter  10 value 94.488307
iter  20 value 94.389947
final  value 94.354692 
converged
Fitting Repeat 3 

# weights:  305
initial  value 116.010067 
iter  10 value 94.488680
iter  20 value 94.366672
iter  30 value 85.425582
iter  40 value 85.398365
iter  50 value 85.390381
iter  60 value 85.217562
final  value 85.151263 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.856597 
iter  10 value 89.470974
iter  20 value 89.358754
iter  30 value 89.123138
iter  40 value 87.670069
iter  50 value 86.244970
iter  60 value 86.021822
iter  70 value 86.019165
iter  80 value 85.731145
iter  90 value 85.623474
iter 100 value 85.623304
final  value 85.623304 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 96.184010 
iter  10 value 94.359136
iter  20 value 94.354787
iter  30 value 94.354014
iter  40 value 86.684990
iter  50 value 86.550313
iter  50 value 86.550313
iter  50 value 86.550313
final  value 86.550313 
converged
Fitting Repeat 1 

# weights:  507
initial  value 102.794942 
iter  10 value 92.882234
iter  20 value 92.483520
iter  30 value 92.234936
iter  40 value 91.968558
iter  50 value 87.718444
iter  60 value 85.351851
iter  70 value 84.893132
iter  80 value 83.601339
iter  90 value 83.276220
iter 100 value 83.275961
final  value 83.275961 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 121.828921 
iter  10 value 87.764920
iter  20 value 85.297041
iter  30 value 85.290726
iter  40 value 85.288189
iter  50 value 84.780246
iter  60 value 84.653434
iter  70 value 84.489756
final  value 84.476414 
converged
Fitting Repeat 3 

# weights:  507
initial  value 102.402715 
iter  10 value 94.258081
iter  20 value 89.545479
iter  30 value 86.961598
iter  40 value 86.487705
iter  50 value 86.328231
iter  60 value 86.321684
iter  70 value 86.053726
iter  80 value 85.904925
iter  90 value 85.901732
final  value 85.899645 
converged
Fitting Repeat 4 

# weights:  507
initial  value 111.219052 
iter  10 value 94.492049
iter  20 value 94.412360
iter  30 value 91.095884
iter  40 value 90.306082
iter  50 value 89.504277
iter  60 value 89.384360
iter  70 value 89.370683
final  value 89.370413 
converged
Fitting Repeat 5 

# weights:  507
initial  value 113.916670 
iter  10 value 94.471141
iter  20 value 93.792788
iter  30 value 86.550019
iter  40 value 86.374397
iter  50 value 86.373827
iter  60 value 86.328187
iter  70 value 84.403928
iter  80 value 82.884593
iter  90 value 82.425178
iter 100 value 81.829357
final  value 81.829357 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 96.338751 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.385050 
iter  10 value 93.895882
iter  20 value 87.717081
final  value 87.588679 
converged
Fitting Repeat 3 

# weights:  103
initial  value 105.328003 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 105.276019 
final  value 94.043243 
converged
Fitting Repeat 5 

# weights:  103
initial  value 108.742907 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 99.361394 
final  value 94.011429 
converged
Fitting Repeat 2 

# weights:  305
initial  value 104.105574 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 99.768081 
iter  10 value 94.043244
final  value 94.043243 
converged
Fitting Repeat 4 

# weights:  305
initial  value 115.527606 
final  value 94.043243 
converged
Fitting Repeat 5 

# weights:  305
initial  value 108.794161 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 104.982897 
iter  10 value 93.355404
iter  20 value 87.572133
final  value 87.571429 
converged
Fitting Repeat 2 

# weights:  507
initial  value 128.273942 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  507
initial  value 126.809663 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  507
initial  value 97.415342 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 108.559266 
iter  10 value 93.899997
iter  20 value 87.706021
iter  30 value 87.421792
iter  40 value 87.411187
iter  40 value 87.411186
iter  40 value 87.411186
final  value 87.411186 
converged
Fitting Repeat 1 

# weights:  103
initial  value 102.114422 
iter  10 value 94.047664
iter  20 value 90.185856
iter  30 value 89.204808
iter  40 value 88.205848
iter  50 value 87.342952
iter  60 value 87.324976
iter  70 value 87.198815
iter  80 value 87.167191
iter  90 value 87.133552
iter 100 value 87.083004
final  value 87.083004 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.265620 
iter  10 value 94.046342
iter  20 value 90.843000
iter  30 value 89.547327
iter  40 value 87.875885
iter  50 value 87.209417
iter  60 value 87.146354
iter  70 value 87.004563
final  value 86.959838 
converged
Fitting Repeat 3 

# weights:  103
initial  value 101.949229 
iter  10 value 94.031497
iter  20 value 90.589362
iter  30 value 90.146918
iter  40 value 88.849612
iter  50 value 88.014596
iter  60 value 86.469153
iter  70 value 85.506018
iter  80 value 84.976437
iter  90 value 84.777418
iter 100 value 84.603938
final  value 84.603938 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 115.858010 
iter  10 value 94.998799
iter  20 value 93.928444
iter  30 value 87.834695
iter  40 value 86.949660
iter  50 value 86.536867
iter  60 value 86.054281
iter  70 value 85.347109
iter  80 value 85.045314
iter  90 value 84.608155
final  value 84.603930 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.282363 
iter  10 value 94.031715
iter  20 value 88.712692
iter  30 value 88.171682
iter  40 value 87.988243
iter  50 value 87.604749
iter  60 value 87.363335
iter  70 value 86.636353
iter  80 value 86.442728
final  value 86.442640 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.617717 
iter  10 value 94.192047
iter  20 value 94.099536
iter  30 value 91.560938
iter  40 value 89.398444
iter  50 value 86.399686
iter  60 value 84.730993
iter  70 value 84.215814
iter  80 value 84.064206
iter  90 value 83.517796
iter 100 value 82.974604
final  value 82.974604 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 101.287657 
iter  10 value 94.060951
iter  20 value 91.635482
iter  30 value 87.302926
iter  40 value 86.806851
iter  50 value 85.649832
iter  60 value 85.017054
iter  70 value 84.633902
iter  80 value 84.437138
iter  90 value 84.385485
iter 100 value 84.344084
final  value 84.344084 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.052071 
iter  10 value 94.043871
iter  20 value 90.690899
iter  30 value 88.363865
iter  40 value 87.456791
iter  50 value 87.152104
iter  60 value 86.953305
iter  70 value 85.747625
iter  80 value 85.267699
iter  90 value 84.680962
iter 100 value 84.626921
final  value 84.626921 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 115.833862 
iter  10 value 94.048969
iter  20 value 93.232742
iter  30 value 93.037784
iter  40 value 87.612471
iter  50 value 86.666668
iter  60 value 85.006873
iter  70 value 83.859689
iter  80 value 83.549444
iter  90 value 83.494142
iter 100 value 83.276782
final  value 83.276782 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 101.717009 
iter  10 value 89.315360
iter  20 value 88.489271
iter  30 value 87.450476
iter  40 value 86.569840
iter  50 value 84.533581
iter  60 value 84.121175
iter  70 value 84.013744
iter  80 value 83.720644
iter  90 value 83.095273
iter 100 value 82.882039
final  value 82.882039 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 130.101862 
iter  10 value 96.566837
iter  20 value 92.841058
iter  30 value 91.904986
iter  40 value 89.938104
iter  50 value 86.504605
iter  60 value 86.283079
iter  70 value 86.015468
iter  80 value 85.685138
iter  90 value 85.203855
iter 100 value 84.616072
final  value 84.616072 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 106.020372 
iter  10 value 94.151074
iter  20 value 89.392222
iter  30 value 87.850594
iter  40 value 87.202674
iter  50 value 86.879449
iter  60 value 86.848052
iter  70 value 86.786347
iter  80 value 86.724110
iter  90 value 86.637876
iter 100 value 85.559184
final  value 85.559184 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.792938 
iter  10 value 94.072639
iter  20 value 93.980235
iter  30 value 91.428385
iter  40 value 86.428134
iter  50 value 85.655092
iter  60 value 84.085796
iter  70 value 83.592814
iter  80 value 83.165336
iter  90 value 82.866644
iter 100 value 82.746590
final  value 82.746590 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 135.802041 
iter  10 value 94.626658
iter  20 value 89.624871
iter  30 value 88.344500
iter  40 value 87.807897
iter  50 value 87.302271
iter  60 value 87.221418
iter  70 value 86.896981
iter  80 value 86.724708
iter  90 value 86.055719
iter 100 value 85.515868
final  value 85.515868 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.009722 
iter  10 value 94.001320
iter  20 value 90.363792
iter  30 value 88.793787
iter  40 value 86.844014
iter  50 value 86.219277
iter  60 value 85.881491
iter  70 value 85.811364
iter  80 value 85.433504
iter  90 value 84.180393
iter 100 value 83.508686
final  value 83.508686 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 104.722537 
iter  10 value 93.287792
iter  20 value 87.071946
iter  30 value 86.324113
iter  40 value 85.609923
iter  50 value 85.593629
final  value 85.592953 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.513863 
iter  10 value 94.054464
iter  20 value 93.759514
iter  30 value 88.032280
iter  40 value 88.016257
iter  50 value 88.013032
iter  60 value 88.008547
iter  70 value 86.273371
iter  80 value 85.983664
final  value 85.981144 
converged
Fitting Repeat 3 

# weights:  103
initial  value 102.650072 
final  value 94.054576 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.204562 
final  value 94.054460 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.397232 
final  value 94.054561 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.400710 
iter  10 value 94.005421
iter  20 value 94.003323
iter  30 value 93.954107
iter  40 value 89.183841
iter  50 value 86.263401
iter  60 value 86.183424
final  value 86.183382 
converged
Fitting Repeat 2 

# weights:  305
initial  value 114.566777 
iter  10 value 94.056846
iter  20 value 94.051691
iter  30 value 92.120485
iter  40 value 87.652822
iter  50 value 87.612375
iter  60 value 86.839306
iter  70 value 84.988886
iter  80 value 84.083795
iter  90 value 84.082209
iter 100 value 84.078641
final  value 84.078641 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 102.973888 
iter  10 value 93.152766
iter  20 value 93.149855
iter  30 value 93.146131
iter  40 value 93.146064
iter  50 value 93.145897
iter  60 value 93.092149
iter  70 value 93.091991
final  value 93.091919 
converged
Fitting Repeat 4 

# weights:  305
initial  value 95.745146 
iter  10 value 94.056837
iter  20 value 94.048213
final  value 94.043279 
converged
Fitting Repeat 5 

# weights:  305
initial  value 105.041652 
iter  10 value 93.909157
iter  20 value 93.343108
iter  30 value 91.002201
iter  40 value 90.831842
iter  50 value 90.831398
iter  60 value 90.830994
iter  70 value 89.515637
iter  80 value 86.236838
iter  90 value 85.829500
iter 100 value 85.811654
final  value 85.811654 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 96.401312 
iter  10 value 94.051383
iter  20 value 94.043362
iter  30 value 94.043288
final  value 94.043280 
converged
Fitting Repeat 2 

# weights:  507
initial  value 111.910150 
iter  10 value 94.060192
iter  20 value 94.003540
iter  30 value 89.025394
iter  40 value 88.242298
iter  50 value 87.925254
iter  60 value 87.066523
iter  70 value 86.821056
iter  80 value 86.783129
iter  90 value 86.744553
iter 100 value 86.744437
final  value 86.744437 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 107.176859 
iter  10 value 94.059819
iter  20 value 91.723137
iter  30 value 90.044875
iter  40 value 89.956474
iter  50 value 89.954489
final  value 89.954441 
converged
Fitting Repeat 4 

# weights:  507
initial  value 113.060739 
iter  10 value 94.060639
iter  20 value 94.053147
iter  30 value 93.980492
iter  40 value 93.294274
iter  50 value 92.833770
iter  60 value 92.584009
iter  70 value 92.562788
iter  80 value 92.562685
iter  90 value 92.438915
iter 100 value 91.791424
final  value 91.791424 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 96.317592 
iter  10 value 94.061363
iter  20 value 94.009443
iter  30 value 88.251882
iter  40 value 88.088031
iter  50 value 83.580622
iter  60 value 82.648170
iter  70 value 82.523660
iter  80 value 82.483086
iter  90 value 82.200923
iter 100 value 82.117483
final  value 82.117483 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 97.477978 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 103.654002 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 116.303022 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 102.978425 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.866900 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 97.302957 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.057779 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.176587 
iter  10 value 93.758422
iter  20 value 90.450179
iter  30 value 89.300168
iter  40 value 89.254660
iter  50 value 88.955231
iter  60 value 88.952730
final  value 88.952715 
converged
Fitting Repeat 4 

# weights:  305
initial  value 102.801565 
final  value 94.354396 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.115916 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 96.117065 
iter  10 value 85.216229
final  value 85.211792 
converged
Fitting Repeat 2 

# weights:  507
initial  value 104.827913 
final  value 94.354396 
converged
Fitting Repeat 3 

# weights:  507
initial  value 129.532490 
iter  10 value 90.341868
iter  20 value 87.174031
iter  30 value 87.075253
iter  40 value 87.054623
iter  50 value 84.138957
iter  60 value 84.081131
iter  70 value 83.961651
iter  80 value 83.960712
final  value 83.960672 
converged
Fitting Repeat 4 

# weights:  507
initial  value 99.907501 
final  value 94.484211 
converged
Fitting Repeat 5 

# weights:  507
initial  value 95.421974 
final  value 94.289216 
converged
Fitting Repeat 1 

# weights:  103
initial  value 112.225646 
iter  10 value 94.489929
iter  20 value 94.477722
iter  30 value 94.311144
iter  40 value 94.299524
iter  50 value 91.611929
iter  60 value 91.117924
iter  70 value 91.049053
iter  80 value 86.521331
iter  90 value 85.454628
iter 100 value 82.633779
final  value 82.633779 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 96.482381 
iter  10 value 88.036645
iter  20 value 85.369784
iter  30 value 85.223083
iter  40 value 84.483413
iter  50 value 84.353158
iter  60 value 83.891466
iter  70 value 83.589644
iter  80 value 83.564715
iter  90 value 83.550520
final  value 83.549370 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.065810 
iter  10 value 94.489621
iter  20 value 94.438775
iter  30 value 84.978760
iter  40 value 84.289105
iter  50 value 83.051332
iter  60 value 82.786327
iter  70 value 82.668286
iter  80 value 82.664680
iter  90 value 82.659225
final  value 82.659215 
converged
Fitting Repeat 4 

# weights:  103
initial  value 107.174918 
iter  10 value 94.495752
iter  20 value 94.478836
iter  30 value 94.307351
iter  40 value 93.656935
iter  50 value 87.951249
iter  60 value 85.817675
iter  70 value 85.580530
iter  80 value 84.661978
iter  90 value 84.441052
iter 100 value 83.855590
final  value 83.855590 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  103
initial  value 110.682726 
iter  10 value 94.543599
iter  20 value 94.490590
iter  30 value 94.438253
iter  40 value 88.151435
iter  50 value 87.656741
iter  60 value 86.308929
iter  70 value 85.966586
iter  80 value 85.908920
iter  90 value 83.246752
iter 100 value 83.084370
final  value 83.084370 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 111.952161 
iter  10 value 94.766532
iter  20 value 94.494326
iter  30 value 94.311937
iter  40 value 94.218419
iter  50 value 87.654253
iter  60 value 85.941776
iter  70 value 84.215970
iter  80 value 83.641772
iter  90 value 82.942525
iter 100 value 82.078475
final  value 82.078475 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 109.785569 
iter  10 value 94.666478
iter  20 value 87.286300
iter  30 value 83.897517
iter  40 value 83.321001
iter  50 value 83.049028
iter  60 value 82.894345
iter  70 value 82.872196
iter  80 value 82.758131
iter  90 value 81.980460
iter 100 value 81.857889
final  value 81.857889 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 106.716480 
iter  10 value 94.961723
iter  20 value 94.135445
iter  30 value 86.135390
iter  40 value 84.950724
iter  50 value 84.328492
iter  60 value 83.371828
iter  70 value 81.440114
iter  80 value 80.585226
iter  90 value 80.425619
iter 100 value 80.304021
final  value 80.304021 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.097392 
iter  10 value 93.773294
iter  20 value 84.914999
iter  30 value 82.778972
iter  40 value 82.505043
iter  50 value 82.312285
iter  60 value 81.674464
iter  70 value 81.532053
iter  80 value 81.201801
iter  90 value 80.449884
iter 100 value 80.274615
final  value 80.274615 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 114.805154 
iter  10 value 94.323070
iter  20 value 84.805428
iter  30 value 83.114815
iter  40 value 82.010458
iter  50 value 80.834657
iter  60 value 79.770647
iter  70 value 79.349080
iter  80 value 79.176348
iter  90 value 79.152204
iter 100 value 79.145325
final  value 79.145325 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 121.652309 
iter  10 value 94.619795
iter  20 value 87.449552
iter  30 value 84.486445
iter  40 value 82.620065
iter  50 value 81.813488
iter  60 value 81.280766
iter  70 value 81.203420
iter  80 value 81.114652
iter  90 value 80.695408
iter 100 value 80.273872
final  value 80.273872 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 116.547608 
iter  10 value 94.614927
iter  20 value 84.677431
iter  30 value 83.973461
iter  40 value 82.085833
iter  50 value 81.209114
iter  60 value 80.555946
iter  70 value 80.318256
iter  80 value 80.011200
iter  90 value 79.740984
iter 100 value 79.678325
final  value 79.678325 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 105.614384 
iter  10 value 97.725400
iter  20 value 87.619097
iter  30 value 87.242974
iter  40 value 84.752761
iter  50 value 81.440542
iter  60 value 80.902924
iter  70 value 80.746678
iter  80 value 80.516424
iter  90 value 80.082198
iter 100 value 79.973039
final  value 79.973039 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 103.443821 
iter  10 value 88.407378
iter  20 value 84.072501
iter  30 value 82.208857
iter  40 value 82.032258
iter  50 value 81.200496
iter  60 value 80.467209
iter  70 value 80.234221
iter  80 value 79.644593
iter  90 value 79.410396
iter 100 value 79.269324
final  value 79.269324 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 108.568673 
iter  10 value 89.715397
iter  20 value 85.295440
iter  30 value 84.330433
iter  40 value 82.759098
iter  50 value 82.578556
iter  60 value 80.994706
iter  70 value 80.423115
iter  80 value 80.081741
iter  90 value 79.662322
iter 100 value 79.420235
final  value 79.420235 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 99.711065 
iter  10 value 88.346588
iter  20 value 84.883746
iter  30 value 84.770972
iter  40 value 84.768241
iter  50 value 84.762203
iter  60 value 84.347543
iter  70 value 84.346463
final  value 84.346453 
converged
Fitting Repeat 2 

# weights:  103
initial  value 106.452329 
final  value 94.485917 
converged
Fitting Repeat 3 

# weights:  103
initial  value 111.422933 
final  value 94.485887 
converged
Fitting Repeat 4 

# weights:  103
initial  value 101.999083 
iter  10 value 85.962257
iter  20 value 85.957730
final  value 85.957212 
converged
Fitting Repeat 5 

# weights:  103
initial  value 107.883866 
final  value 94.485948 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.193945 
iter  10 value 94.304448
iter  20 value 94.301182
iter  30 value 94.299214
iter  40 value 94.298926
iter  50 value 93.878825
iter  60 value 91.542381
iter  70 value 83.800672
iter  80 value 83.479157
iter  90 value 83.477765
iter 100 value 82.914578
final  value 82.914578 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.853901 
iter  10 value 94.489269
iter  20 value 94.484470
iter  30 value 94.107372
iter  40 value 83.957423
iter  50 value 83.156979
iter  60 value 82.850785
iter  70 value 82.789883
final  value 82.789308 
converged
Fitting Repeat 3 

# weights:  305
initial  value 100.585215 
iter  10 value 94.488864
iter  20 value 94.484273
iter  30 value 94.131849
iter  40 value 84.617029
iter  50 value 82.233677
iter  60 value 82.214176
iter  70 value 82.213263
iter  80 value 82.212373
iter  90 value 82.181216
iter 100 value 82.176577
final  value 82.176577 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 106.114330 
iter  10 value 94.488983
iter  20 value 93.030287
iter  30 value 91.195510
iter  40 value 91.194752
final  value 91.193825 
converged
Fitting Repeat 5 

# weights:  305
initial  value 119.893858 
iter  10 value 94.359342
iter  20 value 94.354528
iter  30 value 93.653458
iter  40 value 83.577158
iter  50 value 83.473501
iter  60 value 83.472315
iter  70 value 83.471945
iter  80 value 83.461389
final  value 83.461387 
converged
Fitting Repeat 1 

# weights:  507
initial  value 101.502262 
iter  10 value 93.606933
iter  20 value 93.546015
iter  30 value 86.959286
iter  40 value 86.121383
iter  50 value 86.068519
iter  60 value 86.056762
final  value 86.056545 
converged
Fitting Repeat 2 

# weights:  507
initial  value 97.983469 
iter  10 value 94.362697
iter  20 value 94.359233
iter  30 value 94.355185
iter  40 value 94.244941
iter  50 value 92.289894
iter  60 value 92.034101
iter  70 value 84.931418
iter  80 value 84.177173
iter  90 value 83.770126
iter 100 value 83.728475
final  value 83.728475 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 110.276472 
iter  10 value 94.494580
iter  20 value 94.484769
iter  30 value 94.356893
iter  40 value 94.250678
iter  50 value 84.831678
iter  60 value 83.030319
iter  60 value 83.030318
iter  60 value 83.030318
final  value 83.030318 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.714334 
iter  10 value 94.263054
iter  20 value 94.256617
iter  30 value 94.255081
iter  40 value 94.254917
iter  50 value 83.618082
iter  60 value 83.394425
iter  70 value 83.393777
final  value 83.393762 
converged
Fitting Repeat 5 

# weights:  507
initial  value 116.278974 
iter  10 value 90.311422
iter  20 value 90.307634
iter  30 value 88.557565
iter  40 value 86.069394
iter  50 value 84.723641
iter  60 value 80.969008
iter  70 value 80.628230
iter  80 value 80.524816
iter  90 value 80.250330
iter 100 value 79.548352
final  value 79.548352 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.106616 
final  value 94.052910 
converged
Fitting Repeat 2 

# weights:  103
initial  value 96.245243 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  103
initial  value 98.812486 
final  value 94.052910 
converged
Fitting Repeat 4 

# weights:  103
initial  value 97.327550 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.547977 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  305
initial  value 103.532156 
iter  10 value 93.312378
final  value 93.288889 
converged
Fitting Repeat 2 

# weights:  305
initial  value 98.374485 
final  value 94.052910 
converged
Fitting Repeat 3 

# weights:  305
initial  value 118.188391 
iter  10 value 93.352162
final  value 93.328261 
converged
Fitting Repeat 4 

# weights:  305
initial  value 100.298227 
iter  10 value 93.328261
iter  10 value 93.328261
iter  10 value 93.328261
final  value 93.328261 
converged
Fitting Repeat 5 

# weights:  305
initial  value 118.945980 
final  value 94.052910 
converged
Fitting Repeat 1 

# weights:  507
initial  value 110.834137 
iter  10 value 93.328269
final  value 93.328261 
converged
Fitting Repeat 2 

# weights:  507
initial  value 93.896130 
iter  10 value 93.328296
final  value 93.328261 
converged
Fitting Repeat 3 

# weights:  507
initial  value 130.254951 
iter  10 value 93.332618
final  value 93.328261 
converged
Fitting Repeat 4 

# weights:  507
initial  value 98.787901 
final  value 94.052910 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.833493 
iter  10 value 93.791434
iter  20 value 90.367176
iter  30 value 85.668912
iter  40 value 85.440051
iter  50 value 85.415892
iter  60 value 85.407711
final  value 85.407697 
converged
Fitting Repeat 1 

# weights:  103
initial  value 96.798378 
iter  10 value 93.988163
iter  20 value 89.693204
iter  30 value 88.102907
iter  40 value 86.976200
iter  50 value 83.372708
iter  60 value 82.369095
iter  70 value 81.090955
iter  80 value 80.547820
iter  90 value 79.701916
iter 100 value 79.530512
final  value 79.530512 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  103
initial  value 100.980367 
iter  10 value 94.016556
iter  20 value 86.698397
iter  30 value 81.894777
iter  40 value 81.411838
iter  50 value 81.344646
iter  60 value 81.197059
iter  70 value 81.171843
final  value 81.171800 
converged
Fitting Repeat 3 

# weights:  103
initial  value 103.527484 
iter  10 value 94.057514
iter  20 value 93.995714
iter  30 value 93.701622
iter  40 value 93.676388
iter  50 value 93.386170
iter  60 value 93.381567
iter  70 value 90.754833
iter  80 value 87.633732
iter  90 value 87.037919
iter 100 value 86.637766
final  value 86.637766 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 100.742532 
iter  10 value 94.067475
iter  20 value 94.057155
iter  30 value 84.104083
iter  40 value 81.766000
iter  50 value 80.966808
iter  60 value 80.813584
iter  70 value 80.669950
iter  80 value 80.645550
iter  80 value 80.645549
iter  80 value 80.645549
final  value 80.645549 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.926741 
iter  10 value 94.055323
iter  20 value 93.588210
iter  30 value 93.408684
iter  40 value 86.686682
iter  50 value 83.477851
iter  60 value 83.138968
iter  70 value 82.911574
iter  80 value 81.406382
iter  90 value 81.196733
iter 100 value 81.185857
final  value 81.185857 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  305
initial  value 119.455913 
iter  10 value 94.181289
iter  20 value 89.508193
iter  30 value 82.030893
iter  40 value 81.329356
iter  50 value 81.050808
iter  60 value 80.854082
iter  70 value 80.771609
iter  80 value 80.301877
iter  90 value 79.378473
iter 100 value 78.218072
final  value 78.218072 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.820886 
iter  10 value 89.325759
iter  20 value 85.043941
iter  30 value 83.027772
iter  40 value 81.454869
iter  50 value 79.294959
iter  60 value 78.617192
iter  70 value 78.411323
iter  80 value 78.196527
iter  90 value 78.169901
iter 100 value 78.114017
final  value 78.114017 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  305
initial  value 109.107394 
iter  10 value 93.384299
iter  20 value 88.993229
iter  30 value 84.970704
iter  40 value 84.002447
iter  50 value 82.650009
iter  60 value 80.693775
iter  70 value 80.048173
iter  80 value 79.705797
iter  90 value 79.072377
iter 100 value 78.203501
final  value 78.203501 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 105.545174 
iter  10 value 94.068371
iter  20 value 93.940704
iter  30 value 86.041976
iter  40 value 85.111537
iter  50 value 81.704308
iter  60 value 79.765227
iter  70 value 78.764003
iter  80 value 78.553807
iter  90 value 78.422178
iter 100 value 78.053912
final  value 78.053912 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.971067 
iter  10 value 94.136891
iter  20 value 93.878882
iter  30 value 91.656478
iter  40 value 82.980226
iter  50 value 82.483505
iter  60 value 81.461261
iter  70 value 80.978001
iter  80 value 80.756767
iter  90 value 80.642364
iter 100 value 80.637863
final  value 80.637863 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 116.432144 
iter  10 value 97.932151
iter  20 value 91.088527
iter  30 value 90.045073
iter  40 value 85.030523
iter  50 value 80.389977
iter  60 value 79.440989
iter  70 value 78.844605
iter  80 value 78.277578
iter  90 value 78.093792
iter 100 value 77.984319
final  value 77.984319 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 113.888871 
iter  10 value 94.776342
iter  20 value 86.440189
iter  30 value 82.288872
iter  40 value 81.277649
iter  50 value 79.613649
iter  60 value 78.960547
iter  70 value 78.010632
iter  80 value 77.432379
iter  90 value 77.338341
iter 100 value 77.246498
final  value 77.246498 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 111.196535 
iter  10 value 93.575976
iter  20 value 84.578585
iter  30 value 82.279245
iter  40 value 81.024220
iter  50 value 80.267249
iter  60 value 79.663766
iter  70 value 79.140075
iter  80 value 78.871379
iter  90 value 78.598669
iter 100 value 78.415896
final  value 78.415896 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 104.347737 
iter  10 value 94.191273
iter  20 value 93.979867
iter  30 value 93.910857
iter  40 value 92.756418
iter  50 value 86.196429
iter  60 value 83.397280
iter  70 value 80.153207
iter  80 value 79.889684
iter  90 value 79.223442
iter 100 value 78.982433
final  value 78.982433 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 114.909961 
iter  10 value 99.021571
iter  20 value 95.802880
iter  30 value 93.463429
iter  40 value 92.410656
iter  50 value 89.864892
iter  60 value 87.715104
iter  70 value 83.637118
iter  80 value 80.317524
iter  90 value 78.630052
iter 100 value 78.050778
final  value 78.050778 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 94.138284 
final  value 94.054700 
converged
Fitting Repeat 2 

# weights:  103
initial  value 105.484963 
final  value 94.054753 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.225630 
final  value 94.054634 
converged
Fitting Repeat 4 

# weights:  103
initial  value 103.131157 
iter  10 value 93.496088
iter  20 value 85.238893
iter  30 value 83.955698
iter  40 value 83.846960
iter  50 value 83.732348
iter  60 value 83.607763
iter  70 value 83.607228
final  value 83.607074 
converged
Fitting Repeat 5 

# weights:  103
initial  value 94.168999 
iter  10 value 93.330813
iter  20 value 93.330181
iter  30 value 92.650880
iter  40 value 87.968139
final  value 87.924328 
converged
Fitting Repeat 1 

# weights:  305
initial  value 96.822399 
iter  10 value 93.333631
iter  20 value 93.332626
iter  30 value 93.299169
iter  40 value 93.285659
iter  50 value 93.285522
final  value 93.285371 
converged
Fitting Repeat 2 

# weights:  305
initial  value 102.533894 
iter  10 value 94.058072
iter  20 value 94.052954
iter  20 value 94.052954
iter  20 value 94.052954
final  value 94.052954 
converged
Fitting Repeat 3 

# weights:  305
initial  value 104.832098 
iter  10 value 94.057986
iter  20 value 85.853582
iter  30 value 82.531368
iter  40 value 80.745460
iter  50 value 79.723697
iter  60 value 78.505992
iter  70 value 78.498413
iter  80 value 77.591520
iter  90 value 77.091858
iter 100 value 76.754935
final  value 76.754935 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 95.831332 
iter  10 value 93.290101
iter  20 value 93.289542
iter  30 value 93.118680
iter  40 value 85.645110
iter  50 value 82.432735
final  value 82.319939 
converged
Fitting Repeat 5 

# weights:  305
initial  value 120.088397 
iter  10 value 94.057644
iter  20 value 93.977296
iter  30 value 93.481619
iter  40 value 92.538440
iter  50 value 85.513920
iter  60 value 82.719165
iter  70 value 82.371085
iter  80 value 82.235869
iter  90 value 81.141457
iter 100 value 80.895506
final  value 80.895506 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 105.458217 
iter  10 value 93.353635
iter  20 value 93.292672
iter  30 value 93.288225
iter  40 value 93.150137
iter  50 value 92.889358
iter  60 value 86.089672
iter  70 value 81.641730
iter  80 value 79.408760
iter  90 value 78.358948
iter 100 value 78.342676
final  value 78.342676 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 107.114379 
iter  10 value 94.061087
iter  20 value 94.037530
iter  30 value 91.595757
iter  40 value 80.193877
iter  50 value 80.103796
iter  60 value 80.101598
iter  70 value 80.100493
iter  80 value 80.001536
iter  90 value 79.823711
iter 100 value 76.950395
final  value 76.950395 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 112.975609 
iter  10 value 93.336826
iter  20 value 93.329442
iter  30 value 93.328627
final  value 93.328625 
converged
Fitting Repeat 4 

# weights:  507
initial  value 104.081988 
iter  10 value 93.847294
iter  20 value 93.297116
iter  30 value 89.220935
iter  40 value 84.160469
iter  40 value 84.160469
iter  40 value 84.160469
final  value 84.160469 
converged
Fitting Repeat 5 

# weights:  507
initial  value 98.177461 
iter  10 value 93.517819
iter  20 value 93.389157
iter  30 value 93.175741
iter  40 value 90.775115
iter  50 value 90.689399
iter  60 value 90.689344
final  value 90.689310 
converged
Fitting Repeat 1 

# weights:  103
initial  value 94.763277 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.211644 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  103
initial  value 116.385463 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  103
initial  value 95.474868 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  103
initial  value 95.328445 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  305
initial  value 94.822486 
iter  10 value 92.053547
iter  20 value 89.207359
iter  30 value 89.151370
iter  40 value 87.752901
iter  50 value 85.561803
final  value 85.522090 
converged
Fitting Repeat 2 

# weights:  305
initial  value 105.179001 
final  value 94.445714 
converged
Fitting Repeat 3 

# weights:  305
initial  value 96.797608 
iter  10 value 94.466823
iter  10 value 94.466823
iter  10 value 94.466823
final  value 94.466823 
converged
Fitting Repeat 4 

# weights:  305
initial  value 111.077490 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.252504 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  507
initial  value 97.844338 
final  value 94.484211 
converged
Fitting Repeat 2 

# weights:  507
initial  value 115.479950 
final  value 94.484211 
converged
Fitting Repeat 3 

# weights:  507
initial  value 126.188503 
final  value 94.484211 
converged
Fitting Repeat 4 

# weights:  507
initial  value 103.394039 
final  value 94.466823 
converged
Fitting Repeat 5 

# weights:  507
initial  value 99.295690 
final  value 94.484211 
converged
Fitting Repeat 1 

# weights:  103
initial  value 109.757461 
iter  10 value 94.492220
iter  20 value 94.121181
iter  30 value 93.682574
iter  40 value 88.203924
iter  50 value 85.923643
iter  60 value 83.846063
iter  70 value 82.753404
iter  80 value 82.280692
final  value 82.275793 
converged
Fitting Repeat 2 

# weights:  103
initial  value 97.236381 
iter  10 value 94.491710
iter  20 value 94.387507
iter  30 value 92.709444
iter  40 value 87.726855
iter  50 value 85.947619
iter  60 value 83.375706
iter  70 value 82.637868
iter  80 value 81.773978
iter  90 value 80.962173
iter 100 value 80.492745
final  value 80.492745 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  103
initial  value 99.523675 
iter  10 value 94.487073
iter  20 value 93.622949
iter  30 value 91.642026
iter  40 value 91.395888
iter  50 value 90.598442
iter  60 value 90.211431
iter  70 value 90.056351
iter  80 value 87.272302
iter  90 value 82.521331
iter 100 value 80.978937
final  value 80.978937 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  103
initial  value 101.445694 
iter  10 value 94.478235
iter  20 value 88.941946
iter  30 value 84.177163
iter  40 value 83.887768
iter  50 value 83.384440
iter  60 value 83.338868
iter  70 value 82.637670
iter  80 value 82.295048
iter  90 value 82.275986
final  value 82.275793 
converged
Fitting Repeat 5 

# weights:  103
initial  value 102.048322 
iter  10 value 94.505672
iter  20 value 85.958265
iter  30 value 84.227673
iter  40 value 83.227881
iter  50 value 82.232141
iter  60 value 81.209139
iter  70 value 80.526326
final  value 80.515366 
converged
Fitting Repeat 1 

# weights:  305
initial  value 124.145578 
iter  10 value 94.217510
iter  20 value 84.189598
iter  30 value 82.650368
iter  40 value 81.792313
iter  50 value 81.473736
iter  60 value 81.463050
iter  70 value 81.332063
iter  80 value 80.066053
iter  90 value 79.057634
iter 100 value 78.889467
final  value 78.889467 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 100.641756 
iter  10 value 95.434339
iter  20 value 94.334656
iter  30 value 90.634802
iter  40 value 87.568744
iter  50 value 85.197555
iter  60 value 83.990516
iter  70 value 82.716008
iter  80 value 80.241770
iter  90 value 79.474197
final  value 79.413211 
converged
Fitting Repeat 3 

# weights:  305
initial  value 103.810562 
iter  10 value 94.139171
iter  20 value 91.242243
iter  30 value 90.098987
iter  40 value 89.867171
iter  50 value 86.538127
iter  60 value 82.545686
iter  70 value 79.469961
iter  80 value 78.777697
iter  90 value 78.490581
iter 100 value 78.247541
final  value 78.247541 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 114.741657 
iter  10 value 94.522953
iter  20 value 94.477371
iter  30 value 94.338271
iter  40 value 84.455078
iter  50 value 83.976572
iter  60 value 83.180946
iter  70 value 81.447361
iter  80 value 79.954892
iter  90 value 78.459522
iter 100 value 78.143180
final  value 78.143180 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  305
initial  value 105.067214 
iter  10 value 93.140955
iter  20 value 88.646728
iter  30 value 84.058094
iter  40 value 82.435434
iter  50 value 81.476126
iter  60 value 80.772515
iter  70 value 79.830656
iter  80 value 79.592144
iter  90 value 79.002188
iter 100 value 78.333693
final  value 78.333693 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 124.270439 
iter  10 value 95.219390
iter  20 value 87.766205
iter  30 value 86.425457
iter  40 value 86.238311
iter  50 value 85.450573
iter  60 value 84.448404
iter  70 value 82.007520
iter  80 value 78.666444
iter  90 value 77.857567
iter 100 value 77.464111
final  value 77.464111 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 105.215924 
iter  10 value 94.439383
iter  20 value 92.648384
iter  30 value 85.169652
iter  40 value 84.669807
iter  50 value 83.082818
iter  60 value 79.758800
iter  70 value 78.638780
iter  80 value 77.661232
iter  90 value 77.553952
iter 100 value 77.506229
final  value 77.506229 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 136.125386 
iter  10 value 90.561652
iter  20 value 88.239977
iter  30 value 88.014917
iter  40 value 83.665582
iter  50 value 82.751014
iter  60 value 80.714440
iter  70 value 79.637779
iter  80 value 79.486782
iter  90 value 79.403319
iter 100 value 79.328406
final  value 79.328406 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 112.987285 
iter  10 value 94.102132
iter  20 value 91.471028
iter  30 value 88.298857
iter  40 value 86.373999
iter  50 value 84.109202
iter  60 value 83.031345
iter  70 value 82.051933
iter  80 value 81.111624
iter  90 value 80.420364
iter 100 value 79.284050
final  value 79.284050 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 137.589397 
iter  10 value 94.741801
iter  20 value 87.456366
iter  30 value 84.179991
iter  40 value 79.950337
iter  50 value 79.304096
iter  60 value 79.053821
iter  70 value 78.738155
iter  80 value 78.111248
iter  90 value 77.988730
iter 100 value 77.928027
final  value 77.928027 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  103
initial  value 101.972277 
final  value 94.485945 
converged
Fitting Repeat 2 

# weights:  103
initial  value 99.260272 
final  value 94.485766 
converged
Fitting Repeat 3 

# weights:  103
initial  value 96.148267 
final  value 94.485638 
converged
Fitting Repeat 4 

# weights:  103
initial  value 104.505837 
final  value 94.468363 
converged
Fitting Repeat 5 

# weights:  103
initial  value 98.103192 
final  value 94.485928 
converged
Fitting Repeat 1 

# weights:  305
initial  value 100.118351 
iter  10 value 94.482741
iter  20 value 94.479560
iter  30 value 94.478635
iter  40 value 94.466903
iter  50 value 92.470237
iter  60 value 84.667565
iter  70 value 83.768365
iter  80 value 83.727456
iter  90 value 83.726591
iter 100 value 83.723596
final  value 83.723596 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  305
initial  value 96.442545 
iter  10 value 94.488939
iter  20 value 94.439205
iter  30 value 90.506842
iter  40 value 88.896045
iter  50 value 88.884533
iter  60 value 87.174054
iter  70 value 86.046434
iter  80 value 86.045635
final  value 86.045120 
converged
Fitting Repeat 3 

# weights:  305
initial  value 95.976292 
iter  10 value 94.488392
iter  20 value 94.257120
iter  30 value 90.363191
iter  40 value 90.359570
iter  50 value 90.343277
iter  60 value 90.322501
iter  70 value 85.283391
iter  80 value 83.046295
iter  90 value 82.538777
iter 100 value 82.535152
final  value 82.535152 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  305
initial  value 100.314790 
iter  10 value 91.506714
iter  20 value 91.006176
iter  30 value 91.003727
iter  40 value 90.838645
iter  50 value 90.177222
iter  60 value 90.176709
final  value 90.176687 
converged
Fitting Repeat 5 

# weights:  305
initial  value 95.945888 
iter  10 value 94.466609
iter  20 value 94.466146
iter  30 value 94.461747
iter  40 value 93.299138
iter  50 value 89.477576
iter  60 value 81.575111
iter  70 value 81.443608
iter  80 value 81.103773
iter  90 value 80.120024
iter 100 value 79.514678
final  value 79.514678 
stopped after 100 iterations
Fitting Repeat 1 

# weights:  507
initial  value 115.035609 
iter  10 value 94.492539
iter  20 value 94.277641
iter  30 value 93.664374
iter  40 value 83.133904
iter  50 value 82.235589
iter  60 value 82.028763
iter  70 value 82.028082
iter  80 value 79.279832
iter  90 value 77.323913
iter 100 value 76.481487
final  value 76.481487 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 103.804745 
iter  10 value 94.491388
iter  20 value 94.308964
iter  30 value 91.101249
iter  40 value 90.631878
iter  50 value 88.485815
iter  60 value 88.149375
iter  70 value 88.060479
iter  80 value 82.477100
iter  90 value 79.650430
iter 100 value 79.080827
final  value 79.080827 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 109.566883 
iter  10 value 94.475908
iter  20 value 94.471179
iter  30 value 94.469615
iter  40 value 94.466951
iter  50 value 93.737942
iter  60 value 91.008231
iter  70 value 90.998594
iter  80 value 90.997261
final  value 90.997259 
converged
Fitting Repeat 4 

# weights:  507
initial  value 106.522741 
iter  10 value 94.300708
iter  20 value 94.111716
iter  30 value 87.359062
iter  40 value 85.338832
iter  50 value 80.717650
iter  60 value 78.281539
iter  70 value 76.782878
iter  80 value 76.149493
iter  90 value 76.097473
iter 100 value 76.096288
final  value 76.096288 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 111.941845 
iter  10 value 94.492106
iter  20 value 92.099417
iter  30 value 90.761677
iter  40 value 90.752144
iter  50 value 90.725577
final  value 90.714397 
converged
Fitting Repeat 1 

# weights:  507
initial  value 163.134174 
iter  10 value 118.237587
iter  20 value 117.209043
iter  30 value 106.672806
iter  40 value 106.097631
iter  50 value 105.858768
iter  60 value 105.174132
iter  70 value 104.876431
iter  80 value 104.775611
iter  90 value 104.743133
iter 100 value 104.696367
final  value 104.696367 
stopped after 100 iterations
Fitting Repeat 2 

# weights:  507
initial  value 149.875992 
iter  10 value 118.115575
iter  20 value 113.700472
iter  30 value 105.576998
iter  40 value 105.202715
iter  50 value 104.449120
iter  60 value 103.387832
iter  70 value 102.282159
iter  80 value 102.160050
iter  90 value 101.642000
iter 100 value 101.117776
final  value 101.117776 
stopped after 100 iterations
Fitting Repeat 3 

# weights:  507
initial  value 134.622155 
iter  10 value 117.867127
iter  20 value 116.092066
iter  30 value 108.941275
iter  40 value 106.038981
iter  50 value 104.176090
iter  60 value 103.428592
iter  70 value 102.453540
iter  80 value 101.519600
iter  90 value 101.376060
iter 100 value 101.278586
final  value 101.278586 
stopped after 100 iterations
Fitting Repeat 4 

# weights:  507
initial  value 133.970998 
iter  10 value 117.803170
iter  20 value 106.283371
iter  30 value 104.612945
iter  40 value 103.569536
iter  50 value 102.895779
iter  60 value 102.239655
iter  70 value 101.779916
iter  80 value 101.641378
iter  90 value 101.399304
iter 100 value 101.168090
final  value 101.168090 
stopped after 100 iterations
Fitting Repeat 5 

# weights:  507
initial  value 137.395774 
iter  10 value 118.312077
iter  20 value 117.277955
iter  30 value 106.449224
iter  40 value 104.151506
iter  50 value 102.455392
iter  60 value 101.941568
iter  70 value 101.707250
iter  80 value 101.441152
iter  90 value 101.185980
iter 100 value 100.789665
final  value 100.789665 
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 -- Thu Aug 21 21:31:08 2025 
*********************************************** 
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 

 
1 Test Suite : 
HPiP RUnit Tests - 7 test functions, 0 errors, 0 failures
Number of test functions: 7 
Number of errors: 0 
Number of failures: 0 
Warning messages:
1: `repeats` has no meaning for this resampling method. 
2: executing %dopar% sequentially: no parallel backend registered 
> 
> 
> 
> 
> proc.time()
   user  system elapsed 
 43.962   1.652 115.727