* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/BIOdry.Rcheck’
* using R Under development (unstable) (2026-10-28 r90786)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
    GNU Fortran (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
* running under: Fedora Linux 44 (Server Edition)
* using session charset: UTF-8
* current time: 2026-10-29 09:26:15 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘BIOdry/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘BIOdry’ version ‘0.9.5’
* 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 executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘BIOdry’ can be installed ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* 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 use of S3 registration ... 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 ... [17s/17s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* 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 LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking examples ... [21s/22s] OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’ [104s/106s]
 [104s/107s] ERROR
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(BIOdry)
  Loading required package: nlme
  Loading required package: ecodist
  > 
  > test_check("BIOdry")
  Saving _problems/test-frametoLme-refit-nesting-15.R
  Saving _problems/test-frametoLme-residual-alignment-52.R
  Saving _problems/test-frametoLme-residual-alignment-64.R
  Saving _problems/test-frametoLme-residual-alignment-78.R
  Saving _problems/test-frametoLme-residual-alignment-79.R
  Saving _problems/test-plot-frametoLme-nofluc-39.R
  Saving _problems/test-tdForm-default-time-33.R
  Saving _problems/test-type-roles-64.R
  [ FAIL 8 | WARN 0 | SKIP 0 | PASS 544 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-frametoLme-refit-nesting.R:15:5'): refitting fit$model$data with tdForm keeps plot/tree/sample ──
  Error in `solve.default(-val)`: Lapack routine dgesv: system is exactly singular: U[2,2] = 0
  Backtrace:
       ▆
    1. └─BIOdry::frametoLme(dfm2, form = "tdForm") at test-frametoLme-refit-nesting.R:15:5
    2.   ├─base::do.call(nlme::lme, arl)
    3.   ├─nlme (local) `<fn>`(fixed = `<nmGrpdDt[,8]>`, random = `<pdDiag>`, control = `<named list>`)
    4.   └─nlme:::lme.groupedData(...)
    5.     ├─base::do.call(lme, c(list(fixed = form), args))
    6.     ├─nlme (local) `<fn>`(...)
    7.     └─nlme::lme.formula(...)
    8.       └─nlme:::lmeApVar(...)
    9.         ├─base::solve(-val)
   10.         └─base::solve.default(-val)
  ── Error ('test-frametoLme-residual-alignment.R:52:5'): $fluc is aligned on the documented routes ──
  Error in `solve.default(-val)`: Lapack routine dgesv: system is exactly singular: U[2,2] = 0
  Backtrace:
       ▆
    1. ├─BIOdry (local) expect_fluc_is_pearson(frametoLme(rings_rtimes_scacum(), form = "tdForm")) at test-frametoLme-residual-alignment.R:52:5
    2. │ └─base::as.data.frame(fit$model$data) at test-frametoLme-residual-alignment.R:16:5
    3. └─BIOdry::frametoLme(rings_rtimes_scacum(), form = "tdForm") at test-frametoLme-residual-alignment.R:16:5
    4.   ├─base::do.call(nlme::lme, arl)
    5.   ├─nlme (local) `<fn>`(fixed = `<nmGrpdDt[,8]>`, random = `<pdDiag>`, control = `<named list>`)
    6.   └─nlme:::lme.groupedData(...)
    7.     ├─base::do.call(lme, c(list(fixed = form), args))
    8.     ├─nlme (local) `<fn>`(...)
    9.     └─nlme::lme.formula(...)
   10.       └─nlme:::lmeApVar(...)
   11.         ├─base::solve(-val)
   12.         └─base::solve.default(-val)
  ── Failure ('test-frametoLme-residual-alignment.R:64:5'): $fluc stays aligned, and the fit unchanged, when rows are shuffled ──
  Expected `nlme::fixef(fit$model)` to equal `nlme::fixef(ref$model)`.
  Differences:
    `actual`: 7.567306490 -1.799837931 0.000458373
  `expected`: 7.567309998 -1.799838775 0.000458377
  
  Backtrace:
      ▆
   1. └─BIOdry (local) expect_same_fit(fit, ref) at test-frametoLme-residual-alignment.R:64:5
   2.   └─testthat::expect_equal(...) at test-frametoLme-residual-alignment.R:35:5
  ── Failure ('test-frametoLme-residual-alignment.R:78:5'): $fluc stays aligned, and the fit unchanged, when rows are sorted ──
  Expected `nlme::fixef(fit$model)` to equal `nlme::fixef(ref$model)`.
  Differences:
    `actual`: 7.567312369 -1.799839344 0.000458380
  `expected`: 7.567312887 -1.799839473 0.000458381
  
  Backtrace:
      ▆
   1. └─BIOdry (local) expect_same_fit(fit_year, ref) at test-frametoLme-residual-alignment.R:78:5
   2.   └─testthat::expect_equal(...) at test-frametoLme-residual-alignment.R:35:5
  ── Failure ('test-frametoLme-residual-alignment.R:79:5'): $fluc stays aligned, and the fit unchanged, when rows are sorted ──
  Expected `nlme::fixef(fit$model)` to equal `nlme::fixef(ref$model)`.
  Differences:
    `actual`: 7.567310765 -1.799838956 0.000458378
  `expected`: 7.567312887 -1.799839473 0.000458381
  
  Backtrace:
      ▆
   1. └─BIOdry (local) expect_same_fit(fit_sample, ref) at test-frametoLme-residual-alignment.R:79:5
   2.   └─testthat::expect_equal(...) at test-frametoLme-residual-alignment.R:35:5
  ── Failure ('test-plot-frametoLme-nofluc.R:39:5'): plot() works on a fit without $fluc and shows the same points ──
  Expected `b` to equal `a`.
  Differences:
  actual$P16106/17/a vs expected$P16106/17/a
                                           y
  - actual$P16106/17/a[1, ]     -1.722320510
  + expected$P16106/17/a[1, ]   -1.722324043
  - actual$P16106/17/a[2, ]     -2.733977407
  + expected$P16106/17/a[2, ]   -2.733980634
  - actual$P16106/17/a[3, ]     -2.342461992
  + expected$P16106/17/a[3, ]   -2.342465245
  - actual$P16106/17/a[4, ]     -0.702139676
  + expected$P16106/17/a[4, ]   -0.702143235
  - actual$P16106/17/a[5, ]     -0.491450119
  + expected$P16106/17/a[5, ]   -0.491453643
  - actual$P16106/17/a[6, ]     -0.582855634
  + expected$P16106/17/a[6, ]   -0.582859054
  - actual$P16106/17/a[7, ]      1.935006918
  + expected$P16106/17/a[7, ]    1.935003044
  - actual$P16106/17/a[8, ]      3.618010091
  + expected$P16106/17/a[8, ]    3.618006049
  - actual$P16106/17/a[9, ]      1.584888125
  + expected$P16106/17/a[9, ]    1.584884683
  - actual$P16106/17/a[10, ]     0.532585010
  + expected$P16106/17/a[10, ]   0.532581908
  and 96 more ...
  
       actual$P16106/17/a$y | expected$P16106/17/a$y                
   [1] -1.72232050964       - -1.72232404325         [1]            
   [2] -2.73397740696       - -2.73398063446         [2]            
   [3] -2.34246199214       - -2.34246524459         [3]            
   [4] -0.70213967611       - -0.70214323519         [4]            
   [5] -0.49145011916       - -0.49145364346         [5]            
   [6] -0.58285563362       - -0.58285905351         [6]            
   [7] 1.93500691828        - 1.93500304352          [7]            
   [8] 3.61801009110        - 3.61800604934          [8]            
   [9] 1.58488812525        - 1.58488468265          [9]            
  [10] 0.53258500975        - 0.53258190763          [10]           
   ... ...                    ...                    and 96 more ...
  
  actual$P16106/17/b vs expected$P16106/17/b
                                          y
  - actual$P16106/17/b[1, ]     -1.45367168
  + expected$P16106/17/b[1, ]   -1.45366638
  - actual$P16106/17/b[2, ]     -2.08236364
  + expected$P16106/17/b[2, ]   -2.08235829
  - actual$P16106/17/b[3, ]     -1.72926498
  + expected$P16106/17/b[3, ]   -1.72925981
  - actual$P16106/17/b[4, ]     -0.57807095
  + expected$P16106/17/b[4, ]   -0.57806615
  - actual$P16106/17/b[5, ]     -0.85971732
  + expected$P16106/17/b[5, ]   -0.85971254
  - actual$P16106/17/b[6, ]     -2.08500829
  + expected$P16106/17/b[6, ]   -2.08500332
  - actual$P16106/17/b[7, ]      1.49020825
  + expected$P16106/17/b[7, ]    1.49021233
  - actual$P16106/17/b[8, ]      1.22644515
  + expected$P16106/17/b[8, ]    1.22644923
  - actual$P16106/17/b[9, ]      3.57658978
  + expected$P16106/17/b[9, ]    3.57659336
  - actual$P16106/17/b[10, ]     0.66402451
  + expected$P16106/17/b[10, ]   0.66402870
  and 96 more ...
  
       actual$P16106/17/b$y | expected$P16106/17/b$y                
   [1] -1.453671677         - -1.453666375           [1]            
   [2] -2.082363639         - -2.082358290           [2]            
   [3] -1.729264980         - -1.729259814           [3]            
   [4] -0.578070951         - -0.578066145           [4]            
   [5] -0.859717317         - -0.859712538           [5]            
   [6] -2.085008290         - -2.085003323           [6]            
   [7] 1.490208253          - 1.490212327            [7]            
   [8] 1.226445148          - 1.226449229            [8]            
   [9] 3.576589780          - 3.576593357            [9]            
  [10] 0.664024505          - 0.664028696            [10]           
   ... ...                    ...                    and 96 more ...
  
  actual$P16106/18/a vs expected$P16106/18/a
                                          y
  - actual$P16106/18/a[1, ]     -0.01345372
  + expected$P16106/18/a[1, ]   -0.01345960
  - actual$P16106/18/a[2, ]     -0.42345576
  + expected$P16106/18/a[2, ]   -0.42346130
  - actual$P16106/18/a[3, ]      0.07902726
  + expected$P16106/18/a[3, ]    0.07902190
  - actual$P16106/18/a[4, ]     -0.75304758
  + expected$P16106/18/a[4, ]   -0.75305259
  - actual$P16106/18/a[5, ]     -0.71378580
  + expected$P16106/18/a[5, ]   -0.71379067
  - actual$P16106/18/a[6, ]     -0.24133359
  + expected$P16106/18/a[6, ]   -0.24133838
  - actual$P16106/18/a[7, ]      0.11864589
  + expected$P16106/18/a[7, ]    0.11864125
  - actual$P16106/18/a[8, ]      0.36113091
  + expected$P16106/18/a[8, ]    0.36112647
  - actual$P16106/18/a[9, ]      0.53054765
  + expected$P16106/18/a[9, ]    0.53054343
  - actual$P16106/18/a[10, ]     0.33506760
  + expected$P16106/18/a[10, ]   0.33506363
  and 96 more ...
  
       actual$P16106/18/a$y | expected$P16106/18/a$y                
   [1] -0.01345372          - -0.01345960            [1]            
   [2] -0.42345576          - -0.42346130            [2]            
   [3] 0.07902726           - 0.07902190             [3]            
   [4] -0.75304758          - -0.75305259            [4]            
   [5] -0.71378580          - -0.71379067            [5]            
   [6] -0.24133359          - -0.24133838            [6]            
   [7] 0.11864589           - 0.11864125             [7]            
   [8] 0.36113091           - 0.36112647             [8]            
   [9] 0.53054765           - 0.53054343             [9]            
  [10] 0.33506760           - 0.33506363             [10]           
   ... ...                    ...                    and 96 more ...
  
  actual$P16106/18/b vs expected$P16106/18/b
                                            y
  - actual$P16106/18/b[1, ]     -0.3930905476
  + expected$P16106/18/b[1, ]   -0.3930949811
  - actual$P16106/18/b[2, ]     -1.1329504927
  + expected$P16106/18/b[2, ]   -1.1329543827
  - actual$P16106/18/b[3, ]     -0.5740845515
  + expected$P16106/18/b[3, ]   -0.5740881162
  - actual$P16106/18/b[4, ]     -0.7791526145
  + expected$P16106/18/b[4, ]   -0.7791557560
  - actual$P16106/18/b[5, ]     -0.5969267858
  + expected$P16106/18/b[5, ]   -0.5969295895
  - actual$P16106/18/b[6, ]     -0.1594513657
  + expected$P16106/18/b[6, ]   -0.1594538294
  - actual$P16106/18/b[7, ]      0.3560080240
  + expected$P16106/18/b[7, ]    0.3560059688
  - actual$P16106/18/b[8, ]      0.2168319784
  + expected$P16106/18/b[8, ]    0.2168304029
  - actual$P16106/18/b[9, ]      0.3102935362
  + expected$P16106/18/b[9, ]    0.3102923674
  - actual$P16106/18/b[10, ]    -0.1178800846
  + expected$P16106/18/b[10, ]  -0.1178808415
  and 96 more ...
  
       actual$P16106/18/b$y | expected$P16106/18/b$y                
   [1] -0.39309055          - -0.39309498            [1]            
   [2] -1.13295049          - -1.13295438            [2]            
   [3] -0.57408455          - -0.57408812            [3]            
   [4] -0.77915261          - -0.77915576            [4]            
   [5] -0.59692679          - -0.59692959            [5]            
   [6] -0.15945137          - -0.15945383            [6]            
   [7] 0.35600802           - 0.35600597             [7]            
   [8] 0.21683198           - 0.21683040             [8]            
   [9] 0.31029354           - 0.31029237             [9]            
  [10] -0.11788008          - -0.11788084            [10]           
   ... ...                    ...                    and 96 more ...
  
  actual$P44005/56/a vs expected$P44005/56/a
                                          y
  - actual$P44005/56/a[1, ]      0.29781077
  + expected$P44005/56/a[1, ]    0.29780427
  - actual$P44005/56/a[2, ]      0.67290691
  + expected$P44005/56/a[2, ]    0.67289964
  - actual$P44005/56/a[3, ]      1.64437873
  + expected$P44005/56/a[3, ]    1.64437024
  - actual$P44005/56/a[4, ]     -0.31233897
  + expected$P44005/56/a[4, ]   -0.31234719
  - actual$P44005/56/a[5, ]     -0.28430293
  + expected$P44005/56/a[5, ]   -0.28431133
  - actual$P44005/56/a[6, ]     -1.71438606
  + expected$P44005/56/a[6, ]   -1.71439408
  - actual$P44005/56/a[7, ]     -0.60588866
  + expected$P44005/56/a[7, ]   -0.60589704
  - actual$P44005/56/a[8, ]      0.15307360
  + expected$P44005/56/a[8, ]    0.15306482
  - actual$P44005/56/a[9, ]      0.06308372
  + expected$P44005/56/a[9, ]    0.06307479
  - actual$P44005/56/a[10, ]     0.03247286
  + expected$P44005/56/a[10, ]   0.03246380
  and 132 more ...
  
       actual$P44005/56/a$y | expected$P44005/56/a$y                 
   [1] 0.297810769          - 0.297804266            [1]             
   [2] 0.672906907          - 0.672899637            [2]             
   [3] 1.644378728          - 1.644370241            [3]             
   [4] -0.312338970         - -0.312347189           [4]             
   [5] -0.284302927         - -0.284311332           [5]             
   [6] -1.714386061         - -1.714394084           [6]             
   [7] -0.605888658         - -0.605897038           [7]             
   [8] 0.153073600          - 0.153064825            [8]             
   [9] 0.063083717          - 0.063074791            [9]             
  [10] 0.032472863          - 0.032463801            [10]            
   ... ...                    ...                    and 132 more ...
  
  actual$P44005/56/b vs expected$P44005/56/b
                                           y
  - actual$P44005/56/b[1, ]      0.647876802
  + expected$P44005/56/b[1, ]    0.647896571
  - actual$P44005/56/b[2, ]      0.009093154
  + expected$P44005/56/b[2, ]    0.009112286
  - actual$P44005/56/b[3, ]      0.301909232
  + expected$P44005/56/b[3, ]    0.301927489
  - actual$P44005/56/b[4, ]      0.008454667
  + expected$P44005/56/b[4, ]    0.008472361
  - actual$P44005/56/b[5, ]      0.848969826
  + expected$P44005/56/b[5, ]    0.848986477
  - actual$P44005/56/b[6, ]     -0.178213934
  + expected$P44005/56/b[6, ]   -0.178197511
  - actual$P44005/56/b[7, ]      0.887741234
  + expected$P44005/56/b[7, ]    0.887756685
  - actual$P44005/56/b[8, ]      1.451772580
  + expected$P44005/56/b[8, ]    1.451787059
  - actual$P44005/56/b[9, ]      0.859499185
  + expected$P44005/56/b[9, ]    0.859513232
  - actual$P44005/56/b[10, ]     0.848182571
  + expected$P44005/56/b[10, ]   0.848196095
  and 132 more ...
  
       actual$P44005/56/b$y | expected$P44005/56/b$y                 
   [1] 0.647876802          - 0.647896571            [1]             
   [2] 0.009093154          - 0.009112286            [2]             
   [3] 0.301909232          - 0.301927489            [3]             
   [4] 0.008454667          - 0.008472361            [4]             
   [5] 0.848969826          - 0.848986477            [5]             
   [6] -0.178213934         - -0.178197511           [6]             
   [7] 0.887741234          - 0.887756685            [7]             
   [8] 1.451772580          - 1.451787059            [8]             
   [9] 0.859499185          - 0.859513232            [9]             
  [10] 0.848182571          - 0.848196095            [10]            
   ... ...                    ...                    and 132 more ...
  
  actual$P44005/7/a vs expected$P44005/7/a
                                          y
  - actual$P44005/7/a[1, ]      1.112494189
  + expected$P44005/7/a[1, ]    1.112494533
  - actual$P44005/7/a[2, ]      1.416699263
  + expected$P44005/7/a[2, ]    1.416698985
  - actual$P44005/7/a[3, ]      1.211847575
  + expected$P44005/7/a[3, ]    1.211846952
  - actual$P44005/7/a[4, ]     -0.301708984
  + expected$P44005/7/a[4, ]   -0.301709373
  - actual$P44005/7/a[5, ]      0.105041692
  + expected$P44005/7/a[5, ]    0.105041056
  - actual$P44005/7/a[6, ]     -0.396394859
  + expected$P44005/7/a[6, ]   -0.396395466
  - actual$P44005/7/a[7, ]      0.593078244
  + expected$P44005/7/a[7, ]    0.593077230
  - actual$P44005/7/a[8, ]      0.534803004
  + expected$P44005/7/a[8, ]    0.534801859
  - actual$P44005/7/a[9, ]      0.761805568
  + expected$P44005/7/a[9, ]    0.761804217
  - actual$P44005/7/a[10, ]     1.035504171
  + expected$P44005/7/a[10, ]   1.035502592
  and 132 more ...
  
       actual$P44005/7/a$y | expected$P44005/7/a$y                 
   [1] 1.1124941894        - 1.1124945333          [1]             
   [2] 1.4166992635        - 1.4166989849          [2]             
   [3] 1.2118475751        - 1.2118469523          [3]             
   [4] -0.3017089835       - -0.3017093725         [4]             
   [5] 0.1050416924        - 0.1050410557          [5]             
   [6] -0.3963948593       - -0.3963954657         [6]             
   [7] 0.5930782438        - 0.5930772299          [7]             
   [8] 0.5348030044        - 0.5348018590          [8]             
   [9] 0.7618055678        - 0.7618042169          [9]             
  [10] 1.0355041708        - 1.0355025925          [10]            
   ... ...                   ...                   and 132 more ...
  
  actual$P44005/7/b vs expected$P44005/7/b
                                           y
  - actual$P44005/7/b[1, ]      0.6783657573
  + expected$P44005/7/b[1, ]    0.6783676349
  - actual$P44005/7/b[2, ]      1.1903499313
  + expected$P44005/7/b[2, ]    1.1903514109
  - actual$P44005/7/b[3, ]      0.9078467588
  + expected$P44005/7/b[3, ]    0.9078480966
  - actual$P44005/7/b[4, ]     -1.1107386296
  + expected$P44005/7/b[4, ]   -1.1107368557
  - actual$P44005/7/b[5, ]     -0.4122983113
  + expected$P44005/7/b[5, ]   -0.4122967795
  - actual$P44005/7/b[6, ]     -0.4880022726
  + expected$P44005/7/b[6, ]   -0.4880007900
  - actual$P44005/7/b[7, ]      0.3701553216
  + expected$P44005/7/b[7, ]    0.3701564815
  - actual$P44005/7/b[8, ]      0.4882142645
  + expected$P44005/7/b[8, ]    0.4882152773
  - actual$P44005/7/b[9, ]      0.7569026097
  + expected$P44005/7/b[9, ]    0.7569034276
  - actual$P44005/7/b[10, ]     1.3753891293
  + expected$P44005/7/b[10, ]   1.3753896239
  and 132 more ...
  
       actual$P44005/7/b$y | expected$P44005/7/b$y                 
   [1] 0.6783657573        - 0.6783676349          [1]             
   [2] 1.1903499313        - 1.1903514109          [2]             
   [3] 0.9078467588        - 0.9078480966          [3]             
   [4] -1.1107386296       - -1.1107368557         [4]             
   [5] -0.4122983113       - -0.4122967795         [5]             
   [6] -0.4880022726       - -0.4880007900         [6]             
   [7] 0.3701553216        - 0.3701564815          [7]             
   [8] 0.4882142645        - 0.4882152773          [8]             
   [9] 0.7569026097        - 0.7569034276          [9]             
  [10] 1.3753891293        - 1.3753896239          [10]            
   ... ...                   ...                   and 132 more ...
  
  ── Failure ('test-tdForm-default-time.R:33:5'): the default tdForm on the ringApply help route uses 'time', as 0.9.1 ──
  Expected `unname(nlme::fixef(fit$model))` to equal `ref_091$fixef`.
  Differences:
    `actual`: 7.5673217 -1.7998416 0.0004584
  `expected`: 7.5673024 -1.7998369 0.0004584
  
  ── Error ('test-type-roles.R:64:5'): a factor copy of year is a group and year stays the covariate ──
  Error in `lme.formula(fixed = log(x) ~ log(csx) + year, data = structure(list(x = c(0.629, 1.182, 3.857, 2.03, 2.931, 2.565, 2.092, 2.805, 2.708, 1.312, 2.274, 1.904, 2.604, 1.532, 3.172, 2.014, 2.06, 1.249, 1.195, 1.642, 0.946, 0.467, 1.166, 2.247, 1.08, 0.634, 0.581, 1.314, 0.814, 0.596, 0.96, 2.032, 1.22, 0.862, 1.323, 2.438, 1.117, 0.875, 1.221, 1.735, 1.17, 0.951, 1.163, 1.639, 1.249, 1.205, 0.734, 1.068, 0.491, 0.672, 0.963, 1.23, 0.815, 0.862, 0.621, 0.814, 0.542, 0.482, 0.9, 1.273, 0.756, 0.647, 0.748,  1.149, 0.605, 0.685, 0.381, 0.666, 0.347, 0.304, 1.053, 1.274, 0.866, 0.66, 1.103, 1.318, 0.876, 0.9, 0.368, 0.584, 0.347, 0.165, 0.545, 0.583, 0.43, 0.431, 1.014, 1.154, 0.727, 0.697, 1.382, 1.433, 1.187, 1.002, 0.913, 0.85, 0.538, 0.482, 0.609, 0.659, 0.339, 0.304, 1.154, 1.103, 0.809, 0.698, 1.04, 1.179, 0.697, 0.672, 0.723, 0.697, 0.406, 0.342, 0.545, 0.52, 0.484, 0.355, 0.913, 0.938, 0.626, 0.545, 1.471, 0.317, 0.431, 0.233, 0.19, 1.984, 0.71, 0.762, 0.407, 0.431, 1.081, 0.545, 0.659, 0.408,  0.342, 1.226, 0.583, 0.812, 0.386, 0.279, 0.698, 1.108, 0.431, 0.673, 0.225, 0.178, 0.878, 1.141, 0.672, 0.798, 0.413, 0.393, 0.842, 2.203, 0.659, 0.824, 0.326, 0.228, 0.5, 0.573, 1.956, 2.15, 0.698, 0.812, 0.407, 0.317, 0.305, 0.419, 1.555, 1.454, 0.862, 0.964, 0.446, 0.368, 0.364, 0.49, 1.905, 1.82, 1.065, 0.989, 0.655, 0.647, 0.789, 0.839, 1.244, 1.589, 0.888, 0.748, 0.49, 0.342, 0.863, 0.724, 1.233, 1.669, 0.786, 0.749, 0.591, 0.444, 0.817, 0.398, 1.499, 1.973, 0.596, 0.443, 0.414, 0.228, 2.688,  2.169, 1.723, 2.408, 0.951, 0.685, 0.742, 0.609, 5.91, 1.885, 1.87, 2.16, 0.583, 0.444, 0.442, 0.279, 2.207, 5.694, 1.959, 2.168, 0.799, 0.735, 0.579, 0.647, 1.319, 1.395, 1.729, 1.708, 0.482, 0.457, 0.454, 0.457, 1.116, 1.243, 1.614, 1.637, 0.355, 0.393, 0.265, 0.14, 1.015, 1.509, 2.026, 2.282, 0.977, 0.774, 0.705, 0.609, 1.534, 1.141, 1.466, 1.998, 0.507, 0.507, 0.256, 0.266, 1.103, 0.875, 1.244, 1.559, 0.875, 0.724, 0.455, 0.33, 0.799, 1.395, 1.72, 1.937, 1.382, 1.04, 0.821, 0.634, 1.23, 1.801,  1.637, 2.229, 1.382, 1.078, 0.746, 0.774, 1.522, 1.268, 1.223, 1.474, 0.545, 0.445, 0.356, 0.317, 0.799, 1.433, 1.359, 1.45, 1.065, 0.747, 0.672, 0.621, 0.888, 1.167, 1.281, 1.652, 0.609, 0.52, 0.292, 0.33, 0.888, 1.243, 1.484, 1.915, 0.976, 0.659, 0.468, 0.457, 0.875, 1.322, 1.547, 2.074, 0.837, 0.608, 0.444, 0.444, 1.103, 1.859, 1.719, 1.929, 0.71, 0.533, 0.457, 0.279, 1.332, 1.512, 1.922, 2.049, 0.773, 0.646, 0.291, 0.254, 0.938, 1.237, 1.362, 1.362, 0.558, 0.482, 0.152, 0.19, 0.862, 1.072, 0.697,  0.837, 0.584, 0.444, 0.267, 0.304, 0.609, 1.487, 1.031, 1.362, 0.608, 0.532, 0.254, 0.279, 1.015, 1.57, 0.967, 1.345, 0.71, 0.672, 0.253, 0.342, 0.977, 1.515, 1.257, 1.496, 0.685, 0.571, 0.33, 0.355, 1.053, 1.37, 1.026, 1.248, 0.71, 0.545, 0.127, 0.254, 0.8, 1.303, 1.574, 1.538, 0.634, 0.559, 0.215, 0.254, 1.357, 0.69, 1.428, 1.476, 0.634, 0.545, 0.241, 0.266, 0.888, 0.377, 0.447, 0.633, 0.139, 0.152, 0.076, 0.089, 0.368, 1.046, 1.102, 1.395, 0.368, 0.292, 0.355, 0.241, 0.862, 1.093, 1.174, 1.484,  0.824, 0.558, 0.013, 0.482, 1.027, 0.858, 1.07, 1.37, 0.596, 0.646, 0.405, 0.355, 0.533, 0.833, 0.974, 1.23, 0.634, 1.23, 0.355, 0.406, 0.71, 1.075, 1.103, 1.294, 0.824, 0.901, 0.596, 0.609, 0.85, 0.43, 0.774, 0.786, 0.596, 0.609, 0.521, 0.342, 0.52, 0.493, 0.154, 0.266, 0.622, 0.761, 0.506, 0.456, 0.393, 0.519, 0.868, 0.85, 0.279, 0.495, 0.089, 0.267, 0.469, 0.732, 0.83, 0.761, 0.621, 0.622, 0.419, 0.38, 0.697, 0.781, 0.809, 0.824, 0.659, 0.621, 0.368, 0.406, 0.685, 0.502, 0.584, 0.507, 0.634, 0.57,  0.355, 0.38, 0.419, 0.603, 0.572, 0.469, 0.495, 0.292, 0.228, 0.228, 0.495, 0.604, 0.582, 0.698, 0.85, 0.596, 0.228, 0.33, 0.444, 0.454, 0.373, 0.393, 0.799, 0.761, 0.419, 0.355, 0.254, 0.579, 0.391, 0.571, 0.735, 0.66, 0.444, 0.368, 0.571, 0.567, 0.477, 0.583, 0.609, 0.584, 0.368, 0.241, 0.659, 0.505, 0.412, 0.381, 0.406, 0.469, 0.354, 0.368, 0.507, 0.291, 0.542, 0.456, 0.279, 0.406, 0.128, 0.203, 0.292, 0.202, 0.453, 0.444, 0.304, 0.292, 0.266, 0.19, 0.292, 0.856, 0.298, 0.33, 0.482, 0.558, 0.405,  0.317, 0.824, 0.869, 0.947, 0.888, 0.507, 0.545, 0.609, 0.406, 0.85, 0.713, 1.004, 0.837, 0.393, 0.317, 0.191, 0.114, 0.875, 0.655, 0.703, 0.596, 0.723, 0.71, 0.597, 0.647, 0.672, 0.932, 0.427, 0.368, 0.571, 0.672, 0.468, 0.406, 0.824, 0.615, 0.643, 0.71, 1.015, 0.875, 0.596, 0.482, 0.799, 0.513, 0.755, 0.647, 0.863, 0.837, 0.583, 0.558, 0.634, 0.453, 0.318, 0.279, 0.608, 0.545, 0.596, 0.659, 0.482, 0.402, 0.582, 0.583, 0.659, 0.584, 0.583, 0.621, 0.444, 0.126, 0.611, 0.419, 0.862, 0.786, 0.913,  1.002, 0.279, 0.6, 0.47, 0.355, 0.799, 0.38, 0.432, 0.342, 0.799, 0.575, 0.709, 0.735, 0.951, 0.723, 0.811, 0.761, 0.862, 0.403, 0.561, 0.736, 0.596, 0.47, 0.558, 0.533, 0.38, 0.277, 0.339, 0.596, 0.824, 0.748, 0.596, 0.672, 0.317, 0.599, 0.232, 0.456, 0.533, 0.533, 0.482, 0.393, 0.596, 0.413, 0.659, 0.748, 0.888, 0.925, 0.685, 0.609, 0.342, 0.391, 0.363, 0.558, 0.583, 0.546, 0.304, 0.279, 0.33, 0.453, 0.402, 0.38, 0.621, 0.595, 0.469, 0.38, 0.457, 0.287, 0.328, 0.621, 0.913, 0.939, 0.812, 0.533,  0.304, 0.45, 0.108, 0.266, 0.469, 0.482, 0.329, 0.241, 0.419, 0.464, 0.575, 0.698, 0.888, 0.685, 0.406, 0.469, 0.583, 0.4, 0.514, 0.609, 0.723, 0.558, 0.432, 0.456, 0.444, 0.361, 0.469, 0.533, 0.647, 0.672, 0.419, 0.457, 0.418, 0.485, 0.395, 0.444, 0.659, 0.469, 0.379, 0.456, 0.457, 0.667, 0.491, 0.52, 0.786, 0.583, 0.279, 0.457, 0.571, 0.667, 0.643, 0.862, 0.71, 0.66, 0.292, 0.317, 0.697, 0.423, 0.533, 1.015, 0.824, 0.71, 0.406, 0.342, 0.431, 0.64, 0.528, 0.85, 0.723, 0.749, 0.369, 0.431, 0.634,  0.792, 0.649, 1.205, 0.697, 0.659, 0.253, 0.317, 0.71, 0.452, 0.894, 1.179, 0.647, 0.723, 0.228, 0.419, 0.482, 0.417, 0.512, 0.786, 0.38, 0.532, 0.203, 0.469, 0.495, 0.124, 0.381, 0.495, 0.786, 0.583, 0.292, 0.495, 0.127, 0.235, 0.11, 0.19, 0.533, 0.673, 0.139, 0.254, 0.457, 0.111, 0.45, 0.419, 0.685, 0.634, 0.393, 0.545, 0.241, 0.246, 0.244, 0.609, 0.723, 0.571, 0.279, 0.38, 0.368, 0.355, 0.246, 0.368, 0.558, 0.456, 0.292, 0.254, 0.469, 0.097, 0.328, 0.469, 0.38, 0.406, 0.266, 0.279, 0.203, 0.513,  0.316, 0.418, 0.799, 0.913, 0.723, 0.71, 0.748, 0.498, 0.266, 0.583, 0.545, 0.685, 0.482, 0.368, 0.812, 0.454, 0.3, 0.52, 0.317, 0.456, 0.19, 0.177, 0.786, 0.542, 0.449, 0.672, 0.444, 0.533, 0.381, 0.304, 0.723, 0.219, 0.254, 0.317, 0.254, 0.406, 0.063, 0.063, 0.583, 0.136, 0.223, 0.355, 0.177, 0.291, 0.101, 0.076, 0.457, 0.307, 0.148, 0.241, 0.203, 0.228, 0.09, 0.051, 0.355, 0.198, 0.099, 0.14, 0.38, 0.33, 0.38, 0.051, 0.254, 0.4, 0.22, 0.241, 0.685, 0.546, 0.444, 0.279, 0.634, 0.625, 0.242, 0.495,  0.913, 0.811, 0.443, 0.381, 0.938, 0.642, 0.509, 0.723, 1.23, 0.875, 0.482, 0.33, 0.976, 0.367, 0.218, 0.469, 0.482, 0.469, 0.241, 0.38, 0.507, 0.49, 0.452, 0.495, 0.697, 0.432, 0.33, 0.216, 0.596, 0.221, 0.266, 0.33, 0.444, 0.33, 0.355, 0.203, 0.419, 0.692, 0.408, 0.596, 0.723, 0.684, 0.406, 0.304, 0.926, 0.882, 0.702, 0.786, 1.091, 0.989, 0.748, 0.621, 1.205, 0.638, 0.589, 0.621, 1.002, 0.723, 0.621, 0.368, 0.964, 0.295, 0.215, 0.203, 0.418, 0.241, 0.203, 0.038), csx = c(47.265428671173, 47.687940108643,  51.122428671173, 49.717940108643, 54.053428671173, 52.282940108643, 56.145428671173, 54.464428671173, 46.379940108643, 53.594940108643, 58.419428671173, 56.368428671173, 48.983940108643, 55.126940108643, 61.591428671173, 58.382428671173, 51.043940108643, 56.375940108643, 62.786428671173, 60.024428671173, 51.989940108643, 56.842940108643, 63.952428671173, 62.271428671173, 53.069940108643, 57.476940108643, 64.533428671173, 63.585428671173, 53.883940108643, 58.072940108643, 65.493428671173, 65.617428671173,  55.103940108643, 58.934940108643, 66.816428671173, 68.055428671173, 56.220940108643, 59.809940108643, 68.037428671173, 69.790428671173, 57.390940108643, 60.760940108643, 69.200428671173, 71.429428671173, 58.639940108643, 61.965940108643, 69.934428671173, 72.497428671173, 59.130940108643, 62.637940108643, 70.897428671173, 73.727428671173, 59.945940108643, 63.499940108643, 71.518428671173, 74.541428671173, 60.487940108643, 63.981940108643, 72.418428671173, 75.814428671173, 61.243940108643, 64.628940108643,  73.166428671173, 76.963428671173, 61.848940108643, 65.313940108643, 73.547428671173, 77.629428671173, 62.195940108643, 65.617940108643, 74.600428671173, 78.903428671173, 63.061940108643, 66.277940108643, 75.703428671173, 80.221428671173, 63.937940108643, 67.177940108643, 76.071428671173, 80.805428671173, 64.284940108643, 67.342940108643, 76.616428671173, 81.388428671173, 64.714940108643, 67.773940108643, 77.630428671173, 82.542428671173, 65.441940108643, 68.470940108643, 79.012428671173, 83.975428671173,  66.628940108643, 69.472940108643, 79.925428671173, 84.825428671173, 67.166940108643, 69.954940108643, 80.534428671173, 85.484428671173, 67.505940108643, 70.258940108643, 81.688428671173, 86.587428671173, 68.314940108643, 70.956940108643, 82.728428671173, 87.766428671173, 69.011940108643, 71.628940108643, 83.451428671173, 88.463428671173, 69.417940108643, 71.970940108643, 83.996428671173, 88.983428671173, 69.901940108643, 72.325940108643, 84.909428671173, 89.921428671173, 70.527940108643, 72.870940108643,  31.922529468069, 85.226428671173, 90.352428671173, 70.760940108643, 73.060940108643, 33.906529468069, 85.936428671173, 91.114428671173, 71.167940108643, 73.491940108643, 34.987529468069, 86.481428671173, 91.773428671173, 71.575940108643, 73.833940108643, 36.213529468069, 87.064428671173, 92.585428671173, 71.961940108643, 74.112940108643, 42.012278105526, 37.321529468069, 87.495428671173, 93.258428671173, 72.186940108643, 74.290940108643, 42.890278105526, 38.462529468069, 88.167428671173, 94.056428671173,  72.599940108643, 74.683940108643, 43.732278105526, 40.665529468069, 88.826428671173, 94.880428671173, 72.925940108643, 74.911940108643, 44.232278105526, 42.961278105526, 58.263529468069, 42.815529468069, 89.524428671173, 95.692428671173, 73.332940108643, 75.228940108643, 44.537278105526, 43.380278105526, 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1970L, 1970L, 1970L, 1970L, 1970L, 1970L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1971L, 1972L, 1972L, 1972L,  1972L, 1972L, 1972L, 1972L, 1972L, 1973L, 1973L, 1973L, 1973L, 1973L, 1973L, 1973L, 1973L, 1974L, 1974L, 1974L, 1974L, 1974L, 1974L, 1974L, 1974L, 1975L, 1975L, 1975L, 1975L, 1975L, 1975L, 1975L, 1975L, 1976L, 1976L, 1976L, 1976L, 1976L, 1976L, 1976L, 1976L, 1977L, 1977L, 1977L, 1977L, 1977L, 1977L, 1977L, 1977L, 1978L, 1978L, 1978L, 1978L, 1978L, 1978L, 1978L, 1978L, 1979L, 1979L, 1979L, 1979L, 1979L, 1979L, 1979L, 1979L, 1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1980L, 1981L, 1981L, 1981L,  1981L, 1981L, 1981L, 1981L, 1981L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1982L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1983L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 1984L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1985L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1986L, 1987L, 1987L, 1987L, 1987L, 1987L, 1987L, 1987L, 1987L, 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2005L, 2005L), time = c(NA, NA, NA, NA, NA, NA, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,  10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L, 25L, 25L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 28L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L, 32L, 32L, 32L, 32L, NA, 33L, 33L, 33L, 33L, NA, 34L, 34L, 34L,  34L, NA, 35L, 35L, 35L, 35L, NA, 36L, 36L, 36L, 36L, NA, NA, 37L, 37L, 37L, 37L, NA, NA, 38L, 38L, 38L, 38L, NA, NA, 39L, 39L, 39L, 39L, 4L, 4L, 8L, 8L, 40L, 40L, 40L, 40L, 5L, 5L, 9L, 9L, 41L, 41L, 41L, 41L, 6L, 6L, 10L, 10L, 42L, 42L, 42L, 42L, 7L, 7L, 11L, 11L, 43L, 43L, 43L, 43L, 8L, 8L, 12L, 12L, 44L, 44L, 44L, 44L, 9L, 9L, 13L, 13L, 45L, 45L, 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344L, 847L, 486L, 992L, 94L, 593L, 200L, 706L, 345L, 848L, 487L, 993L, 95L, 594L, 201L, 707L, 346L, 849L, 488L, 994L, 96L, 595L, 202L, 708L, 347L, 850L, 489L, 995L, 97L, 596L, 203L, 709L, 348L, 851L, 490L, 996L, 98L, 597L, 204L, 710L, 349L, 852L, 491L, 997L, 99L, 598L, 205L, 711L, 350L, 853L, 492L, 998L, 100L, 599L, 206L, 712L, 351L, 854L,  493L, 999L, 101L, 600L, 207L, 713L, 352L, 855L, 494L, 1000L, 102L, 601L, 208L, 714L, 353L, 856L, 495L, 1001L, 103L, 602L, 209L, 715L, 354L, 857L, 496L, 1002L, 104L, 603L, 210L, 716L, 355L, 858L, 497L, 1003L, 105L, 604L, 211L, 717L, 356L, 859L, 498L, 1004L, 106L, 605L, 212L, 718L, 357L, 860L, 499L, 1005L, 107L, 606L, 213L, 719L, 358L, 861L, 500L, 1006L, 108L, 607L, 214L, 720L, 359L, 862L, 501L, 1007L, 109L, 608L, 215L, 721L, 360L, 863L, 502L, 1008L), class = c("nfnGroupedData", "nfGroupedData", "groupedData",  "data.frame"), formula = log(x) ~ log(csx) + year | fyear, FUN = function (x)  max(x, na.rm = TRUE), order.groups = TRUE, time_cols = c("year", "time"), split_vars = "fyear", BIOdry_meta = structure(list(inputs = "x", response = "x", active_response = "x", time = "year", time_vars = "year", groups = c("plot", "tree", "sample"), time_in_groups = FALSE, name_map = list(), call = defineDataRoles(rd = x, validate = "none"), history = list(list(fn = "rtimes", lv_input = 2, lv_resolved = "tree", split_vars = c("tree", "plot"), output_names = c("x", "year", "time", "sample", "tree",  "plot"), arg_summary = list(focus = NULL, target = NULL, focus_mode = "auto")), list(fn = "scacum", lv_input = "sample", lv_resolved = "sample", split_vars = c("sample", "tree", "plot"), output_names = c("x", "csx", "year", "time", "sample", "tree", "plot"), arg_summary = list(dotargs = list(sc.c = c(P16106.17 = 122.234278105526, P16106.18 = 139.748529468069, P44005.56 = 158.518428671173, P44005.7 = 114.910940108643), rf.t = 2003), focus = NULL, target = NULL, focus_mode = "auto"))), last_step = list(     fn = "scacum", lv_input = "sample", lv_resolved = "sample", split_vars = c("sample", "tree", "plot"), output_names = c("x", "csx", "year", "time", "sample", "tree", "plot"), arg_summary = list(dotargs = list(sc.c = c(P16106.17 = 122.234278105526, P16106.18 = 139.748529468069, P44005.56 = 158.518428671173, P44005.7 = 114.910940108643), rf.t = 2003), focus = NULL, target = NULL, focus_mode = "auto")), role_hints = list(response = "x", time = "year", groups = c("sample", "tree", "plot")), split_vars = c("sample",  "tree", "plot"), last_fn = "scacum", meta_version = "ringApply-attribute-v3"), class = c("BIOdry_meta", "list"))), random = structure(numeric(0), class = c("pdDiag", "pdMat"), formula = ~log(csx) + year, Dimnames = list(NULL, NULL)), control = list(maxIter = 50, msMaxIter = 200, tolerance = 1e-06, niterEM = 25, msMaxEval = 200, msTol = 1e-07, msVerbose = FALSE, returnObject = FALSE, gradHess = TRUE, apVar = TRUE, .relStep = 6.05545445239334e-06, opt = "nlminb", optimMethod = "BFGS", minAbsParApVar = 0.05,      natural = TRUE, sigma = 0, allow.n.lt.q = FALSE))`: nlminb problem, convergence error code = 1
    message = singular convergence (7)
  Backtrace:
      ▆
   1. └─BIOdry::frametoLme(d2, form = "log(x) ~ log(csx) + year | fyear") at test-type-roles.R:64:5
   2.   ├─base::do.call(nlme::lme, arl)
   3.   ├─nlme (local) `<fn>`(fixed = `<nfnGrpdD[,6]>`, random = `<pdDiag>`, control = `<named list>`)
   4.   └─nlme:::lme.groupedData(...)
   5.     ├─base::do.call(lme, c(list(fixed = form), args))
   6.     ├─nlme (local) `<fn>`(...)
   7.     └─nlme::lme.formula(...)
  
  [ FAIL 8 | WARN 0 | SKIP 0 | PASS 544 ]
  Error:
  ! Test failures.
  Execution halted
* checking PDF version of manual ... OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/localhost/ripley/R/packages/tests-MKL/BIOdry.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 3:00.76, 169.16 + 5.80