* using log directory ‘/data/localhost/ripley/R/packages/tests-noLD/ackwards.Rcheck’
* using R Under development (unstable) (2026-07-25 r90301)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
    GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
* running under: Fedora Linux 44 (Server Edition)
* using session charset: UTF-8
* current time: 2026-07-26 16:43:21 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘ackwards/DESCRIPTION’ ... OK
* this is package ‘ackwards’ version ‘0.1.1’
* 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 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 ‘ackwards’ can be installed ... OK
* checking package directory ... OK
* checking ‘build’ 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 ... [11s/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 installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’ [297s/176s]
 [298s/176s] ERROR
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(ackwards)
  > 
  > test_check("ackwards")
  Starting 2 test processes.
  > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-suggest_k.R: i PA-PC suggested 6 components -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3.
  > test-suggest_k.R: i Increase `k_max` to evaluate th
  > test-suggest_k.R: e full suggestion.
  > test-suggest_k.R: i PA-FA suggested 6 factors -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3.
  > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.
  > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [447ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 364 rows with missing values removed (2436 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [190ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [8.7s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [206ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [155ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [1.4s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-suggest_k.R: i PA-PC suggested 3 components -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.
  > test-suggest_k.R: i Increase `k_max` to evaluate th
  > test-suggest_k.R: e full suggestion.
  > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.
  > test-suggest_k.R: i Increase `k_max` to evaluate the f
  > test-suggest_k.R: ull suggestion.
  > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [329ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 86 rows with missing values removed (914 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [110ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [2.8s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [148ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [148ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [1.1s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [167ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [155ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [1.3s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [91ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [75ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [100ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.
  > test-suggest_k.R: i Increase `k_max` to evaluate the f
  > test-suggest_k.R: ull suggestion.
  > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [295ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [214ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [125ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [500ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [105ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: i CD requires EFAtools (install to enable).
  > test-suggest_k.R: v Running MAP and VSS... [99ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is  used
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is  used
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In smc, smcs < 0 were set to .0
  > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is  used
  > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is  used
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [85ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: x Running MAP and VSS... [65ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [177ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 64 rows with missing values removed (936 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [144ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [1.7s]
  > test-suggest_k.R: 
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 3
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentatio
  > test-suggest_k.R: n is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 3
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA - MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v
  > test-suggest_k.R: k = 2: PA-PC v PA-FA - MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v
  > test-suggest_k.R: k = 3: PA-PC - PA-FA - MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold)
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 3
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA - MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v
  > test-suggest_k.R: k = 2: PA-PC v PA-FA - MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v
  > test-suggest_k.R: k = 3: PA-PC - PA-FA - MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold)
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 3
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: i Running parallel analysis (20 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (20 iterations, PC + FA)... [567ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 125 rows with missing values removed (875 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [283ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [13.6s]
  > test-suggest_k.R: 
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510*
  > test-suggest_k.R: + CD requires EFAtools (install to enable).
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510*
  > test-suggest_k.R: + CD requires EFAtools (install to enable).
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [184ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used).
  > test-suggest_k.R: v Running MAP and VSS... [101ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [410ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v*
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD -
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD -
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 1
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v*
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD -
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD -
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 3
  > test-suggest_k.R: * MAP: k = 1
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: * CD: k = 1
  > test-suggest_k.R: Consensus range: k = 1-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427 VSS-1 0.6224 VSS-2 0.0000
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522* VSS-1 0.7305* VSS-2 0.7981*
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510
  > test-suggest_k.R: + CD requires EFAtools (install to enable).
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 2
  > test-suggest_k.R: * MAP: k = 2
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 2
  > test-suggest_k.R: Consensus: k = 2
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-suggest_k.R: nge.
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427 VSS-1 0.6224 VSS-2 0.0000
  > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522* VSS-1 0.7305* VSS-2 0.7981*
  > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407
  > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510
  > test-suggest_k.R: + CD requires EFAtools (install to enable).
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * PA-PC: k <= 2
  > test-suggest_k.R: * PA-FA: k <= 2
  > test-suggest_k.R: * MAP: k = 2
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 2
  > test-suggest_k.R: Consensus: k = 2
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentatio
  > test-suggest_k.R: n is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [33ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [244ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).
  > test-suggest_k.R: i Running Comparison Data (CD)...
  > test-suggest_k.R: v Running Comparison Data (CD)... [1.9s]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [83ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-suggest_k.R: Variables: 8
  > test-suggest_k.R: n: 1,000
  > test-suggest_k.R: Basis: pearson
  > test-suggest_k.R: Tested k: 1-4
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Criteria (k = 1-4) --
  > test-suggest_k.R: 
  > test-suggest_k.R: k = 1: VSS-1 0.6224 VSS-2 0.0000
  > test-suggest_k.R: k = 2: VSS-1 0.7305* VSS-2 0.7981
  > test-suggest_k.R: k = 3: VSS-1 0.6415 VSS-2 0.8407
  > test-suggest_k.R: k = 4: VSS-1 0.6451 VSS-2 0.8510*
  > test-suggest_k.R: 
  > test-suggest_k.R: -- Recommendations --
  > test-suggest_k.R: 
  > test-suggest_k.R: * VSS-1: k = 2
  > test-suggest_k.R: * VSS-2: k = 4
  > test-suggest_k.R: Consensus range: k = 2-4
  > test-suggest_k.R: --------------------------------------------------------------------------------
  > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.
  > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.
  > test-suggest_k.R: i Running MAP and VSS...
  > test-suggest_k.R: v Running MAP and VSS... [33ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [176ms]
  > test-suggest_k.R: 
  > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...
  > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [180ms]
  > test-suggest_k.R: 
  > test-esem.R: 
  > test-esem.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-esem.R: Engine: esem
  > test-esem.R: Rotation: varimax
  > test-esem.R: Basis: pearson
  > test-esem.R: n: 200
  > test-esem.R: k (max): 3
  > test-esem.R: 
  > test-esem.R: -- Levels --
  > test-esem.R: 
  > test-esem.R: v k = 1: 1 factor, 43.0% variance
  > test-esem.R: v k = 2: 2 factors, 85.2% variance
  > test-esem.R: v k = 3: 3 factors, 87.8% variance
  > test-esem.R: 
  > test-esem.R: -- Edges --
  > test-esem.R: 
  > test-esem.R: 3 of 8 edges have |r| >= 0.3
  > test-esem.R: --------------------------------------------------------------------------------
  > test-esem.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-esem.R: Cross-level edges are descriptive score correlation
  > test-esem.R: s. Per-level fit indices
  > test-esem.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-esem.R: they do not validate the edges or the hierarchy itself.
  > test-layout.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.
  > test-layout.R: i Nodes are retained in the object; inspect with `x$prune$n
  > test-layout.R: odes` and `x$prune$chains`.
  > test-check-items.R: 
  > test-check-items.R: -- Item quality check (ackwards) -----------------------------------------------
  > test-check-items.R: Basis: pearson
  > test-check-items.R: Items: 5
  > test-check-items.R: Flagged: 0
  > test-check-items.R: v No item problems detected.
  > test-check-items.R: --------------------------------------------------------------------------------
  > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one
  > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make
  > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or
  > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame.
  > test-check-items.R: 
  > test-check-items.R: -- Item quality check (ackwards) -----------------------------------------------
  > test-check-items.R: Basis: polychoric
  > test-check-items.R: Items: 7
  > test-check-items.R: Flagged: 2
  > test-check-items.R: 
  > test-check-items.R: -- Flagged items --
  > test-check-items.R: 
  > test-check-items.R: x constant: "const"
  > test-check-items.R: ! near-constant: "nc"
  > test-check-items.R: --------------------------------------------------------------------------------
  > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one
  > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make
  > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or
  > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame.
  > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)...
  > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [151ms]
  > test-comparability.R: 
  > test-comparability.R: 
  > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------
  > test-comparability.R: Engine: pca
  > test-comparability.R: Basis: pearson
  > test-comparability.R: n: 1,000 (500 per half)
  > test-comparability.R: Splits: 2
  > test-comparability.R: Levels: 1-3
  > test-comparability.R: 
  > test-comparability.R: -- Comparability by level (median across splits) --
  > test-comparability.R: 
  > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)
  > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)
  > test-comparability.R: k = 3: median r .73, min r .28 (m3f2) [1/2 splits usable]
  > test-comparability.R: --------------------------------------------------------------------------------
  > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in
  > test-comparability.R: `$coefficients`.
  > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et
  > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --
  > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.
  > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)...
  > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [106ms]
  > test-comparability.R: 
  > test-comparability.R: 
  > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------
  > test-comparability.R: Engine: pca
  > test-comparability.R: Basis: pearson
  > test-comparability.R: n: 1,000 (500 per half)
  > test-comparability.R: Splits: 2
  > test-comparability.R: Levels: 1-3
  > test-comparability.R: 
  > test-comparability.R: -- Comparability by level (median across splits) --
  > test-comparability.R: 
  > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)
  > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)
  > test-comparability.R: k = 3: no usable splits (half-solutions did not converge)
  > test-comparability.R: --------------------------------------------------------------------------------
  > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in
  > test-comparability.R: `$coefficients`.
  > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et
  > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --
  > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.
  > test-comparability.R: 
  > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------
  > test-comparability.R: Engine: pca
  > test-comparability.R: Basis: pearson
  > test-comparability.R: n: 1,000 (500 per half)
  > test-comparability.R: Splits: 4
  > test-comparability.R: Levels: 1-5
  > test-comparability.R: 
  > test-comparability.R: -- Comparability by level (median across splits) --
  > test-comparability.R: 
  > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)
  > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1)
  > test-comparability.R: k = 3: median r .80, min r .58 (m3f2)
  > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2)
  > test-comparability.R: k = 5: median r .99, min r .14 (m5f5)
  > test-comparability.R: --------------------------------------------------------------------------------
  > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in
  > test-comparability.R: `$coefficients`.
  > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et
  > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --
  > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.
  > test-comparability.R: 
  > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------
  > test-comparability.R: Engine: pca
  > test-comparability.R: Basis: pearson
  > test-comparability.R: n: 1,000 (500 per half)
  > test-comparability.R: Splits: 4
  > test-comparability.R: Levels: 1-5
  > test-comparability.R: 
  > test-comparability.R: -- Comparability by level (median across splits) --
  > test-comparability.R: 
  > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)
  > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1)
  > test-comparability.R: k = 3: median r .80, min r .58 (m3f2)
  > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2)
  > test-comparability.R: k = 5: median r .99, min r .14 (m5f5)
  > test-comparability.R: --------------------------------------------------------------------------------
  > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in
  > test-comparability.R: `$coefficients`.
  > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et
  > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --
  > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.
  > test-comparability.R: 
  > test-comparability.R: Likely variables with missing values are  i6  
  > test-comparability.R: 
  > test-comparability.R: Likely variables with missing values are 
  > test-comparability.R:  i6  
  > test-comparability.R: 
  > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------
  > test-comparability.R: Engine: pca
  > test-comparability.R: Basis: pearson
  > test-comparability.R: n: 1,000 (500 per half)
  > test-comparability.R: Splits: 2
  > test-comparability.R: Levels: 1-2 (requested 1-3; full-sample fit truncated)
  > test-comparability.R: 
  > test-comparability.R: -- Comparability by level (median across splits) --
  > test-comparability.R: 
  > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)
  > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)
  > test-comparability.R: --------------------------------------------------------------------------------
  > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in
  > test-comparability.R: `$coefficients`.
  > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et
  > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --
  > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.
  > test-cor-input.R: 
  > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-cor-input.R: Engine: pca
  > test-cor-input.R: Rotation: varimax
  > test-cor-input.R: Basis: (user-supplied matrix)
  > test-cor-input.R: n: NA
  > test-cor-input.R: k (max): 3
  > test-cor-input.R: 
  > test-cor-input.R: -- Levels --
  > test-cor-input.R: 
  > test-cor-input.R: v k = 1: 1 factor, 41.8% variance
  > test-cor-input.R: v k = 2: 2 factors, 58.5% variance
  > test-cor-input.R: v k = 3: 3 factors, 72.2% variance
  > test-cor-input.R: 
  > test-cor-input.R: -- Edges --
  > test-cor-input.R: 
  > test-cor-input.R: 5 of 8 edges have |r| >= 0.3
  > test-cor-input.R: --------------------------------------------------------------------------------
  > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-cor-input.R: they do not validate the edges or the hierarchy itself.
  > test-cor-input.R: 
  > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-cor-input.R: Engine: pca
  > test-cor-input.R: Rotation: varimax
  > test-cor-input.R: Basis: (user-supplied matrix)
  > test-cor-input.R: n: NA
  > test-cor-input.R: k (max): 3
  > test-cor-input.R: 
  > test-cor-input.R: -- Levels --
  > test-cor-input.R: 
  > test-cor-input.R: v k = 1: 1 factor, 41.8% variance
  > test-cor-input.R: v k = 2: 2 factors, 58.5% variance
  > test-cor-input.R: v k = 3: 3 factors, 72.2% variance
  > test-cor-input.R: 
  > test-cor-input.R: -- Edges --
  > test-cor-input.R: 
  > test-cor-input.R: 5 of 8 edges have |r| >= 0.3
  > test-cor-input.R: --------------------------------------------------------------------------------
  > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-cor-input.R: they do not validate the edges or the hierarchy itself.
  > test-data.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-data.R: v Running parallel analysis (5 iterations, PC + FA)... [231ms]
  > test-data.R: 
  > test-data.R: i Running MAP and VSS...
  > test-data.R: v Running MAP and VSS... [219ms]
  > test-data.R: 
  > test-data.R: i Running Comparison Data (CD)...
  > test-cor-input.R: 
  > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-cor-input.R: Variables: 6
  > test-cor-input.R: n: 875
  > test-cor-input.R: Basis: (user-supplied matrix)
  > test-cor-input.R: Tested k: 1-5
  > test-cor-input.R: 
  > test-cor-input.R: -- Criteria (k = 1-5) --
  > test-cor-input.R: 
  > test-cor-input.R: k = 1: PA-PC v PA-FA v MAP 0.0461* VSS-1 0.7025 VSS-2 0.0000
  > test-cor-input.R: k = 2: PA-PC - PA-FA v MAP 0.1279 VSS-1 0.7919* VSS-2 0.8159
  > test-cor-input.R: k = 3: PA-PC - PA-FA v MAP 0.2614 VSS-1 0.7666 VSS-2 0.8879
  > test-cor-input.R: k = 4: PA-PC - PA-FA - MAP 0.4712 VSS-1 0.6796 VSS-2 0.9054*
  > test-cor-input.R: k = 5: PA-PC - PA-FA - MAP 1.0000 VSS-1 0.7009 VSS-2 0.8935
  > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input).
  > test-cor-input.R: 
  > test-cor-input.R: -- Recommendations --
  > test-cor-input.R: 
  > test-cor-input.R: * PA-PC: k <= 1
  > test-cor-input.R: * PA-FA: k <= 3
  > test-cor-input.R: * MAP: k = 1
  > test-cor-input.R: * VSS-1: k = 2
  > test-cor-input.R: * VSS-2: k = 4
  > test-cor-input.R: Consensus range: k = 1-4
  > test-cor-input.R: --------------------------------------------------------------------------------
  > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-cor-input.R: above the consensus to observe factor fragmentation is intentional.
  > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-cor-input.R: nge.
  > test-cor-input.R: 
  > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------
  > test-cor-input.R: Variables: 6
  > test-cor-input.R: n: 875
  > test-cor-input.R: Basis: (user-supplied matrix)
  > test-cor-input.R: Tested k: 1-5
  > test-cor-input.R: 
  > test-cor-input.R: -- Criteria (k = 1-5) --
  > test-cor-input.R: 
  > test-cor-input.R: k = 1: PA-PC v PA-FA v MAP 0.0461* VSS-1 0.7025 VSS-2 0.0000
  > test-cor-input.R: k = 2: PA-PC - PA-FA v MAP 0.1279 VSS-1 0.7919* VSS-2 0.8159
  > test-cor-input.R: k = 3: PA-PC - PA-FA v MAP 0.2614 VSS-1 0.7666 VSS-2 0.8879
  > test-cor-input.R: k = 4: PA-PC - PA-FA - MAP 0.4712 VSS-1 0.6796 VSS-2 0.9054*
  > test-cor-input.R: k = 5: PA-PC - PA-FA - MAP 1.0000 VSS-1 0.7009 VSS-2 0.8935
  > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input).
  > test-cor-input.R: 
  > test-cor-input.R: -- Recommendations --
  > test-cor-input.R: 
  > test-cor-input.R: * PA-PC: k <= 1
  > test-cor-input.R: * PA-FA: k <= 3
  > test-cor-input.R: * MAP: k = 1
  > test-cor-input.R: * VSS-1: k = 2
  > test-cor-input.R: * VSS-2: k = 4
  > test-cor-input.R: Consensus range: k = 1-4
  > test-cor-input.R: --------------------------------------------------------------------------------
  > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels
  > test-cor-input.R: above the consensus to observe factor fragmentation is intentional.
  > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,
  > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the ra
  > test-cor-input.R: nge.
  > test-cor-input.R: i Running parallel analysis (5 iterations, PC + FA)...
  > test-cor-input.R: v Running parallel analysis (5 iterations, PC + FA)... [328ms]
  > test-cor-input.R: 
  > test-cor-input.R: i Running MAP and VSS...
  > test-cor-input.R: v Running MAP and VSS... [276ms]
  > test-cor-input.R: 
  > test-cor-input.R: i Running Comparison Data (CD)...
  > test-cor-input.R: v Running Comparison Data (CD)... [488ms]
  > test-cor-input.R: 
  > test-data.R: v Running Comparison Data (CD)... [4.6s]
  > test-data.R: 
  > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.
  > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.
  > test-efa.R: 
  > test-efa.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-efa.R: Engine: efa
  > test-efa.R: Rotation: varimax
  > test-efa.R: Basis: pearson
  > test-efa.R: n: 2,800
  > test-efa.R: k (max): 3
  > test-efa.R: 
  > test-efa.R: -- Levels --
  > test-efa.R: 
  > test-efa.R: v k = 1: 1 factor, 17.0% variance
  > test-efa.R: v k = 2: 2 factors, 26.1% variance
  > test-efa.R: v k = 3: 3 factors, 32.1% variance
  > test-efa.R: 
  > test-efa.R: -- Edges --
  > test-efa.R: 
  > test-efa.R: 5 of 8 edges have |r| >= 0.3
  > test-efa.R: --------------------------------------------------------------------------------
  > test-efa.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-efa.R: Cross-level edges are descriptive score correlation
  > test-efa.R: s. Per-level fit indices
  > test-efa.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-efa.R: they do not validate the edges or the hierarchy itself.
  > test-efa.R: Error in solve.default(r) : 
  > test-efa.R:   system is computationally singular: reciprocal condition number = 2.96996e-17
  > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.
  > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.
  > test-factorability.R: 
  > test-factorability.R: -- Factorability screen (ackwards) ---------------------------------------------
  > test-factorability.R: Basis: pearson
  > test-factorability.R: Observations: 1,000
  > test-factorability.R: Variables: 16
  > test-factorability.R: N:p ratio: 62.5:1
  > test-factorability.R: 
  > test-factorability.R: -- Sampling adequacy --
  > test-factorability.R: 
  > test-factorability.R: Overall KMO: 0.86 (meritorious)
  > test-factorability.R: 
  > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001
  > test-factorability.R: 
  > test-factorability.R: -- Identifiability --
  > test-factorability.R: 
  > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables
  > test-factorability.R: (EFA/ESEM; PCA is unbounded).
  > test-factorability.R: --------------------------------------------------------------------------------
  > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely
  > test-factorability.R: used *rules of thumb*, not settled thresholds -- required N depends on
  > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the
  > test-factorability.R: numbers, not a pass/fail.
  > test-factorability.R: 
  > test-factorability.R: -- Factorability screen (ackwards) ---------------------------------------------
  > test-factorability.R: Basis: pearson
  > test-factorability.R: Observations: 1,000
  > test-factorability.R: Variables: 16
  > test-factorability.R: N:p ratio: 62.5:1
  > test-factorability.R: 
  > test-factorability.R: -- Sampling adequacy --
  > test-factorability.R: 
  > test-factorability.R: Overall KMO: 0.86 (meritorious)
  > test-factorability.R: 
  > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001
  > test-factorability.R: 
  > test-factorability.R: -- Identifiability --
  > test-factorability.R: 
  > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables
  > test-factorability.R: (EFA/ESEM; PCA is unbounded).
  > test-factorability.R: --------------------------------------------------------------------------------
  > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely
  > test-factorability.R: used *rules of thumb*, not settled thresholds -- 
  > test-factorability.R: required N depends on
  > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the
  > test-factorability.R: numbers, not a pass/fail.
  > test-interpret.R: 
  > test-interpret.R: -- Salient factors by item (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.25
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: x1: First indicator
  > test-interpret.R: m3f1 [-0.939]
  > test-interpret.R: m3f3 [0.279]
  > test-interpret.R: 
  > test-interpret.R: x2
  > test-interpret.R: m3f1 [-0.955]
  > test-interpret.R: 
  > test-interpret.R: x3
  > test-interpret.R: m3f1 [-0.950]
  > test-interpret.R: 
  > test-interpret.R: x4
  > test-interpret.R: m3f2 [0.937]
  > test-interpret.R: 
  > test-interpret.R: x5
  > test-interpret.R: m3f2 [0.952]
  > test-interpret.R: 
  > test-interpret.R: x6
  > test-interpret.R: m3f2 [0.956]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.25
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: m3f1
  > test-interpret.R: x2 [-0.955]
  > test-interpret.R: x3 [-0.950]
  > test-interpret.R: x1: First indicator [-0.939]
  > test-interpret.R: 
  > test-interpret.R: m3f2
  > test-interpret.R: x6 [0.956]
  > test-interpret.R: x5 [0.952]
  > test-interpret.R: x4 [0.937]
  > test-interpret.R: 
  > test-interpret.R: m3f3
  > test-interpret.R: x1: First indicator [0.279]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-label_template.R: c(
  > test-label_template.R:   "m1f1" = "m1f1",
  > test-label_template.R:   "m2f1" = "m2f1",
  > test-label_template.R:   "m2f2" = "m2f2"
  > test-label_template.R: )
  > test-label_template.R: `label_template()` scaffold (id style):
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.3
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 1 (1 factor) --
  > test-interpret.R: 
  > test-interpret.R: m1f1
  > test-interpret.R: x4 [0.709]
  > test-interpret.R: x5 [0.706]
  > test-interpret.R: x6 [0.706]
  > test-interpret.R: x3 [-0.701]
  > test-interpret.R: x2 [-0.676]
  > test-interpret.R: x1 [-0.662]
  > test-interpret.R: 
  > test-interpret.R: -- Level 2 (2 factors) --
  > test-interpret.R: 
  > test-interpret.R: m2f1
  > test-interpret.R: x6 [0.954]
  > test-interpret.R: x5 [0.951]
  > test-interpret.R: x4 [0.946]
  > test-interpret.R: 
  > test-interpret.R: m2f2
  > test-interpret.R: x2 [-0.952]
  > test-interpret.R: x1 [-0.944]
  > test-interpret.R: x3 [-0.941]
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: m3f1
  > test-interpret.R: x2 [-0.955]
  > test-interpret.R: x3 [-0.950]
  > test-interpret.R: x1 [-0.939]
  > test-interpret.R: 
  > test-interpret.R: m3f2
  > test-interpret.R: x6 [0.956]
  > test-interpret.R: x5 [0.952]
  > test-interpret.R: x4 [0.937]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.3
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 1 (1 factor) --
  > test-interpret.R: 
  > test-interpret.R: m1f1
  > test-interpret.R: x4 [0.709]
  > test-interpret.R: x5 [0.706]
  > test-interpret.R: x6 [0.706]
  > test-interpret.R: x3 [-0.701]
  > test-interpret.R: x2 [-0.676]
  > test-interpret.R: x1 [-0.662]
  > test-interpret.R: 
  > test-interpret.R: -- Level 2 (2 factors) --
  > test-interpret.R: 
  > test-interpret.R: m2f1
  > test-interpret.R: x6 [0.954]
  > test-interpret.R: x5 [0.951]
  > test-interpret.R: x4 [0.946]
  > test-interpret.R: 
  > test-interpret.R: m2f2
  > test-interpret.R: x2 [-0.952]
  > test-interpret.R: x1 [-0.944]
  > test-interpret.R: x3 [-0.941]
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: m3f1
  > test-interpret.R: x2 [-0.955]
  > test-interpret.R: x3 [-0.950]
  > test-interpret.R: x1 [-0.939]
  > test-interpret.R: 
  > test-interpret.R: m3f2
  > test-interpret.R: x6 [0.956]
  > test-interpret.R: x5 [0.952]
  > test-interpret.R: x4 [0.937]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.99
  > test-interpret.R: Top-n: all
  > test-interpret.R: No items met the |loading| >= 0.99 threshold.
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.3
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: m3f1
  > test-interpret.R: A4: Love children [0.778]
  > test-interpret.R: A5: Make people feel at ease [0.735]
  > test-interpret.R: A3: Know how to comfort others [0.728]
  > test-interpret.R: A2: Inquire about others' well-being [0.562]
  > test-interpret.R: 
  > test-interpret.R: m3f2
  > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919]
  > test-interpret.R: A2: Inquire about others' well-being [0.529]
  > test-interpret.R: A3: Know how to comfort others [0.387]
  > test-interpret.R: 
  > test-interpret.R: m3f3
  > test-interpret.R: C1: Am exacting in my work [0.995]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-interpret.R: 
  > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------
  > test-interpret.R: Engine: pca
  > test-interpret.R: Cut: |loading| >= 0.3
  > test-interpret.R: Top-n: all
  > test-interpret.R: 
  > test-interpret.R: -- Level 3 (3 factors) --
  > test-interpret.R: 
  > test-interpret.R: m3f1
  > test-interpret.R: A4: Love children [0.778]
  > test-interpret.R: A5: Make people feel at ease [0.735]
  > test-interpret.R: A3: Know how to comfort others [0.728]
  > test-interpret.R: A2: Inquire about others' well-being [0.562]
  > test-interpret.R: 
  > test-interpret.R: m3f2
  > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919]
  > test-interpret.R: A2: Inquire about others' well-being [0.529]
  > test-interpret.R: A3: Know how to comfort others [0.387]
  > test-interpret.R: 
  > test-interpret.R: m3f3
  > test-interpret.R: C1: Am exacting in my work [0.995]
  > test-interpret.R: --------------------------------------------------------------------------------
  > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")
  > test-interpret.R: for the full matrix.
  > test-pca.R: Error in solve.default(r) : 
  > test-pca.R:   Lapack routine dgesv: system is exactly singular: U[6,6] = 0
  > test-print.R: 
  > test-print.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-print.R: Engine: pca
  > test-print.R: Rotation: varimax
  > test-print.R: Basis: pearson
  > test-print.R: n: 2,800
  > test-print.R: k (max): 3
  > test-print.R: 
  > test-print.R: -- Levels --
  > test-print.R: 
  > test-print.R: v k = 1: 1 factor, 20.1% variance
  > test-print.R: v k = 2: 2 factors, 31.1% variance
  > test-print.R: v k = 3: 3 factors, 39.6% variance
  > test-print.R: 
  > test-print.R: -- Edges --
  > test-print.R: 
  > test-print.R: 5 of 8 edges have |r| >= 0.3
  > test-print.R: --------------------------------------------------------------------------------
  > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-print.R: they do not validate the edges or the hierarchy itself.
  > test-print.R: 
  > test-print.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-print.R: Engine: pca
  > test-print.R: Rotation: varimax
  > test-print.R: Basis: pearson
  > test-print.R: n: 2,800
  > test-print.R: k (max): 3
  > test-print.R: 
  > test-print.R: -- Levels --
  > test-print.R: 
  > test-print.R: v k = 1: 1 factor, 20.1% variance
  > test-print.R: v k = 2: 2 factors, 31.1% variance
  > test-print.R: v k = 3: 3 factors, 39.6% variance
  > test-print.R: 
  > test-print.R: -- Edges --
  > test-print.R: 
  > test-print.R: 5 of 8 edges have |r| >= 0.3
  > test-print.R: --------------------------------------------------------------------------------
  > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-print.R: Cross-level edges are descriptive score correlation
  > test-print.R: s. Per-level fit indices
  > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-print.R: they do not validate the edges or the hierarchy itself.
  > test-print.R: 
  > test-print.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------
  > test-print.R: Engine: pca
  > test-print.R: Rotation: varimax
  > test-print.R: Basis: pearson
  > test-print.R: n: 2,800
  > test-print.R: k (max): 3
  > test-print.R: 
  > test-print.R: -- Levels --
  > test-print.R: 
  > test-print.R: k = 1: 1 factor (30.2% cumulative variance)
  > test-print.R: m1f1 30.2% eigenvalue 3.02
  > test-print.R: 
  > test-print.R: k = 2: 2 factors (48.1% cumulative variance)
  > test-print.R: m2f1 24.5% eigenvalue 3.02
  > test-print.R: m2f2 23.6% eigenvalue 1.79
  > test-print.R: 
  > test-print.R: k = 3: 3 factors (57.4% cumulative variance)
  > test-print.R: m3f1 24.1% eigenvalue 3.02
  > test-print.R: m3f2 22.1% eigenvalue 1.79
  > test-print.R: m3f3 11.2% eigenvalue 0.93
  > test-print.R: 
  > test-print.R: -- Lineage (primary parents) --
  > test-print.R: 
  > test-print.R: m1f1 > m2f1, m2f2
  > test-print.R: m2f1 > m3f1
  > test-print.R: m2f2 > m3f2, m3f3
  > test-print.R: --------------------------------------------------------------------------------
  > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-print.R: they do not validate the edges or the hierarchy itself.
  > test-print.R: 
  > test-print.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------
  > test-print.R: Engine: pca
  > test-print.R: Rotation: varimax
  > test-print.R: Basis: pearson
  > test-print.R: n: 2,800
  > test-print.R: k (max): 3
  > test-print.R: 
  > test-print.R: -- Levels --
  > test-print.R: 
  > test-print.R: k = 1: 1 factor (30.2% cumulative variance)
  > test-print.R: m1f1 30.2% eigenvalue 3.02
  > test-print.R: 
  > test-print.R: k = 2: 2 factors (48.1% cumulative variance)
  > test-print.R: m2f1 24.5% eigenvalue 3.02
  > test-print.R: m2f2 23.6% eigenvalue 1.79
  > test-print.R: 
  > test-print.R: k = 3: 3 factors (57.4% cumulative variance)
  > test-print.R: m3f1 24.1% eigenvalue 3.02
  > test-print.R: m3f2 22.1% eigenvalue 1.79
  > test-print.R: m3f3 11.2% eigenvalue 0.93
  > test-print.R: 
  > test-print.R: -- Lineage (primary parents) --
  > test-print.R: 
  > test-print.R: m1f1 > m2f1, m2f2
  > test-print.R: m2f1 > m3f1
  > test-print.R: m2f2 > m3f2, m3f3
  > test-print.R: --------------------------------------------------------------------------------
  > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-print.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: Error in solve.default(r) : 
  > test-prune.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: Error in solve.default(r) : 
  > test-prune.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: Error in solve.default(r) : 
  > test-prune.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 500
  > test-prune.R: k (max): 4
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 97.3% variance
  > test-prune.R: v k = 2: 2 factors, 99.8% variance
  > test-prune.R: v k = 3: 3 factors, 99.8% variance
  > test-prune.R: v k = 4: 4 factors, 99.9% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 8 of 20 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Redundancy (direct, |r| >= 0.9): 6 nodes flagged
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 500
  > test-prune.R: k (max): 4
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 97.3% variance
  > test-prune.R: v k = 2: 2 factors, 99.8% variance
  > test-prune.R: v k = 3: 3 factors, 99.8% variance
  > test-prune.R: v k = 4: 4 factors, 99.9% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 8 of 20 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Redundancy (direct, |r| >= 0.9): 6 nodes flagged
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 1,000
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 41.8% variance
  > test-prune.R: v k = 2: 2 factors, 58.5% variance
  > test-prune.R: v k = 3: 3 factors, 72.2% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 5 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs
  > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 1,000
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 41.8% variance
  > test-prune.R: v k = 2: 2 factors, 58.5% variance
  > test-prune.R: v k = 3: 3 factors, 72.2% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 5 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs
  > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 500
  > test-prune.R: k (max): 4
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 97.3% variance
  > test-prune.R: v k = 2: 2 factors, 99.8% variance
  > test-prune.R: v k = 3: 3 factors, 99.8% variance
  > test-prune.R: v k = 4: 4 factors, 99.9% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 8 of 20 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Redundancy (direct, |r| >= 0.9, phi > 0.9): 5 nodes flagged
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 500
  > test-prune.R: k (max): 4
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 97.3% variance
  > test-prune.R: v k = 2: 2 factors, 99.8% variance
  > test-prune.R: v k = 3: 3 factors, 99.8% variance
  > test-prune.R: v k = 4: 4 factors, 99.9% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 8 of 20 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Redundancy (direct, |r| >= 0.9, phi > 0.9): 5 nodes flagged
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-scores.R: Error in solve.default(r) : 
  > test-scores.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 150
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 20.9% variance
  > test-prune.R: v k = 2: 2 factors, 38.4% variance
  > test-prune.R: v k = 3: 3 factors, 55.5% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 6 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs
  > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 150
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 20.9% variance
  > test-prune.R: v k = 2: 2 factors, 38.4% variance
  > test-prune.R: v k = 3: 3 factors, 55.5% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 6 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs
  > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 150
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: k = 1: 1 factor (20.9% cumulative variance)
  > test-prune.R: m1f1 20.9% eigenvalue 1.25
  > test-prune.R: 
  > test-prune.R: k = 2: 2 factors (38.4% cumulative variance)
  > test-prune.R: m2f1 20.3% eigenvalue 1.25
  > test-prune.R: m2f2 18.1% eigenvalue 1.05
  > test-prune.R: 
  > test-prune.R: k = 3: 3 factors (55.5% cumulative variance)
  > test-prune.R: m3f1 19.5% eigenvalue 1.25
  > test-prune.R: m3f2 18.4% eigenvalue 1.05
  > test-prune.R: m3f3 17.6% eigenvalue 1.02
  > test-prune.R: 
  > test-prune.R: -- Lineage (primary parents) --
  > test-prune.R: 
  > test-prune.R: m1f1 > m2f1, m2f2
  > test-prune.R: m2f1 > m3f1
  > test-prune.R: m2f2 > m3f2, m3f3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level pairs
  > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object with all edges preserved.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the 
  > test-prune.R: edges or the hierarchy itself.
  > test-prune.R: Error in solve.default(r) : 
  > test-prune.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: Error in solve.default(r) : 
  > test-prune.R:   Lapack routine dgesv: system is exactly singular: U[4,4] = 0
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 100
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 21.2% variance
  > test-prune.R: v k = 2: 2 factors, 40.9% variance
  > test-prune.R: v k = 3: 3 factors, 57.5% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 5 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Manual: 1 node explicitly flagged (m2f1)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 100
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: v k = 1: 1 factor, 21.2% variance
  > test-prune.R: v k = 2: 2 factors, 40.9% variance
  > test-prune.R: v k = 3: 3 factors, 57.5% variance
  > test-prune.R: 
  > test-prune.R: -- Edges --
  > test-prune.R: 
  > test-prune.R: 5 of 8 edges have |r| >= 0.3
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Manual: 1 node explicitly flagged (m2f1)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object; all edges are preserved. Inspec
  > test-prune.R: t with `x$prune$nodes` and
  > test-prune.R: `tidy(x, what = "nodes")`.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 100
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: k = 1: 1 factor (21.2% cumulative variance)
  > test-prune.R: m1f1 21.2% eigenvalue 1.27
  > test-prune.R: 
  > test-prune.R: k = 2: 2 factors (40.9% cumulative variance)
  > test-prune.R: m2f1 21.1% eigenvalue 1.27
  > test-prune.R: m2f2 19.7% eigenvalue 1.18
  > test-prune.R: 
  > test-prune.R: k = 3: 3 factors (57.5% cumulative variance)
  > test-prune.R: m3f1 19.5% eigenvalue 1.27
  > test-prune.R: m3f2 19.4% eigenvalue 1.18
  > test-prune.R: m3f3 18.6% eigenvalue 1.00
  > test-prune.R: 
  > test-prune.R: -- Lineage (primary parents) --
  > test-prune.R: 
  > test-prune.R: m1f1 > m2f1, m2f2
  > test-prune.R: m2f1 > m3f1, m3f3
  > test-prune.R: m2f2 > m3f2
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Manual: 1 node explicitly flagged (m2f1)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object with all edges preserved.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  > test-prune.R: 
  > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------
  > test-prune.R: Engine: pca
  > test-prune.R: Rotation: varimax
  > test-prune.R: Basis: pearson
  > test-prune.R: n: 100
  > test-prune.R: k (max): 3
  > test-prune.R: 
  > test-prune.R: -- Levels --
  > test-prune.R: 
  > test-prune.R: k = 1: 1 factor (21.2% cumulative variance)
  > test-prune.R: m1f1 21.2% eigenvalue 1.27
  > test-prune.R: 
  > test-prune.R: k = 2: 2 factors (40.9% cumulative variance)
  > test-prune.R: m2f1 21.1% eigenvalue 1.27
  > test-prune.R: m2f2 19.7% eigenvalue 1.18
  > test-prune.R: 
  > test-prune.R: k = 3: 3 factors (57.5% cumulative variance)
  > test-prune.R: m3f1 19.5% eigenvalue 1.27
  > test-prune.R: m3f2 19.4% eigenvalue 1.18
  > test-prune.R: m3f3 18.6% eigenvalue 1.00
  > test-prune.R: 
  > test-prune.R: -- Lineage (primary parents) --
  > test-prune.R: 
  > test-prune.R: m1f1 > m2f1, m2f2
  > test-prune.R: m2f1 > m3f1, m3f3
  > test-prune.R: m2f2 > m3f2
  > test-prune.R: 
  > test-prune.R: -- Pruning --
  > test-prune.R: 
  > test-prune.R: Manual: 1 node explicitly flagged (m2f1)
  > test-prune.R: --------------------------------------------------------------------------------
  > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes
  > test-prune.R: remain in the object with all edges preserved.
  > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.
  > test-prune.R: Cross-level edges are descriptive score correlation
  > test-prune.R: s. Per-level fit indices
  > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --
  > test-prune.R: they do not validate the edges or the hierarchy itself.
  Saving _problems/test-prune-667.R
  Saving _problems/test-prune-836.R
  [ FAIL 2 | WARN 0 | SKIP 11 | PASS 2283 ]
  
  ══ Skipped tests (11) ══════════════════════════════════════════════════════════
  • NEWS.md not reachable from this test context (1):
    'test-docs-no-milestone-refs.R:20:3'
  • On CRAN (5): 'test-print-snapshot.R:16:1', 'test-print-snapshot.R:23:1',
    'test-print-snapshot.R:29:1', 'test-print-snapshot.R:38:1',
    'test-print-snapshot.R:48:1'
  • README files not reachable from this test context (1):
    'test-docs-no-milestone-refs.R:31:3'
  • data-raw/sim16.R not reachable from this test context (1):
    'test-data.R:173:3'
  • inst/CITATION not reachable from this test context (1):
    'test-docs-citations.R:43:3'
  • man/ackwards.Rd not reachable from this test context (1):
    'test-docs-citations.R:21:3'
  • vignettes/ not reachable from this test context (1):
    'test-docs-no-milestone-refs.R:42:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-prune.R:667:3'): EFA prune='redundant' auto-applies redundancy_phi=0.95 and announces ──
  Error in `parent_signs[parents[j]]`: invalid subscript type 'list'
  Backtrace:
      ▆
   1. └─ackwards::ackwards(data, k_max = 4, engine = "efa")
   2.   └─ackwards:::.align_signs(...)
  ── Error ('test-prune.R:836:3'): auto-phi flagged set is a subset of the |r|-only set (EFA, end to end) ──
  Error in `parent_signs[parents[j]]`: invalid subscript type 'list'
  Backtrace:
      ▆
   1. └─ackwards::ackwards(data, k_max = 4, engine = "efa")
   2.   └─ackwards:::.align_signs(...)
  
  [ FAIL 2 | WARN 0 | SKIP 11 | PASS 2283 ]
  Error:
  ! Test failures.
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [31s/36s] OK
* 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-noLD/ackwards.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 4:48.57, 358.21 + 17.76