* using log directory ‘/Users/ripley/R/packages/tests-devel/colocboost.Rcheck’
* using R Under development (unstable) (2026-06-06 r90114)
* using platform: aarch64-apple-darwin25.5.0
* R was compiled by
    Apple clang version 21.0.0 (clang-2100.1.1.101)
    GNU Fortran (GCC) 14.2.0
* running under: macOS Tahoe 26.5.1
* using session charset: UTF-8
* current time: 2026-06-07 07:37:09 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘colocboost/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘colocboost’ version ‘1.0.8’
* 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 ‘colocboost’ can be installed ... [10s/10s] OK
* checking installed package size ... INFO
  installed size is  5.2Mb
  sub-directories of 1Mb or more:
    data   2.2Mb
    doc    1.9Mb
* 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 ... OK
* checking Rd files ... OK
* checking Rd metadata ... 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’ [29s/30s]
 [30s/30s] ERROR
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
  > # * https://testthat.r-lib.org/articles/special-files.html
  > 
  > library(testthat)
  > library(colocboost)
  > 
  > test_check("colocboost")
  Validating input data.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  No trait-specific (uncolocalized) effects in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  There are 1 uCoS generated after filtering the robust colocalization.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Extracting colocalization results with cos_npc_cutoff = 0.2 and npc_outcome_cutoff = 0.1.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.1.
  Validating input data.
  Validating input data.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 59 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 2 iterations!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  No ambiguous colocalization events!
  There exists the ambiguous colocalization events from trait-specific effects. Extracting!
  There are 1 ambiguous trait-specific effects.
  There exists the ambiguous colocalization events from trait-specific effects. Extracting!
  There are 1 ambiguous trait-specific effects.
  There exists the ambiguous colocalization events from trait-specific effects. Extracting!
  There are 1 ambiguous trait-specific effects.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
  Extracting colocalization results with cos_npc_cutoff = 0.8 and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.8. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
  Extracting colocalization results with pvalue_cutoff = 0.05, cos_npc_cutoff = 0.5, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 3 converged after 92 iterations!
  Gradient boosting for outcome 1 converged after 95 iterations!
  Gradient boosting for outcome 2 converged after 99 iterations!
  Performing inference on colocalization events.
  All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
  All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 3 converged after 92 iterations!
  Gradient boosting for outcome 1 converged after 95 iterations!
  Gradient boosting for outcome 2 converged after 99 iterations!
  Performing inference on colocalization events.
  All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
  All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
  There exists the ambiguous colocalization events from trait-specific effects. Extracting!
  There are 1 ambiguous trait-specific effects.
  No ambiguous colocalization events!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  There exists the ambiguous colocalization events from trait-specific effects. Extracting!
  There are 1 ambiguous trait-specific effects.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 109 iterations!
  Gradient boosting for outcome 3 converged after 111 iterations!
  Gradient boosting for outcome 2 converged after 117 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 109 iterations!
  Gradient boosting for outcome 3 converged after 111 iterations!
  Gradient boosting for outcome 2 converged after 117 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  No ambiguous colocalization events!
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
  Keep only uCoS with npc_outcome_cutoff >= 0.2. 
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
  Keep only uCoS with npc_outcome_cutoff >= 0.1. 
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.8.
  Keep only uCoS with npc_outcome_cutoff >= 0.8. 
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
  Keep only uCoS with npc_outcome_cutoff >= 1. 
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
  Keep only uCoS with npc_outcome_cutoff >= 0.2. 
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
  Keep only uCoS with npc_outcome_cutoff >= 0.2. 
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
  Keep only uCoS with npc_outcome_cutoff >= 1. 
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
  Keep only uCoS with npc_outcome_cutoff >= 0.2. 
  Saving _problems/test_inference-1335.R
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 71 iterations!
  Gradient boosting for outcome 3 converged after 98 iterations!
  Gradient boosting for outcome 2 converged after 112 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
  Keep only uCoS with npc_outcome_cutoff >= 0.1. 
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.3.
  Keep only uCoS with npc_outcome_cutoff >= 0.3. 
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.5.
  Keep only uCoS with npc_outcome_cutoff >= 0.5. 
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.7.
  Keep only uCoS with npc_outcome_cutoff >= 0.7. 
  Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.9.
  Keep only uCoS with npc_outcome_cutoff >= 0.9. 
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001.
  For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
  There are 1 uCoS generated after filtering the robust colocalization.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
  Keep only uCoS with pvalue of variants for the outcome < 1e-05.
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
  Keep only uCoS with pvalue of variants for the outcome < 1e-05.
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
  Keep only uCoS with pvalue of variants for the outcome < 1e-05.
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 1 converged after 68 iterations!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Show all CoSs to uncolocalized outcomes.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Show all CoSs to uncolocalized outcomes.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for focal outcome 4 converged after 68 iterations!
  Gradient boosting for outcome 2 converged after 152 iterations!
  Gradient boosting for outcome 3 converged after 161 iterations!
  Gradient boosting for outcome 1 converged after 162 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
  For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
  Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
  Show all CoSs to uncolocalized outcomes.
  Extracting colocalization results with cos_npc_cutoff = 0.7 and npc_outcome_cutoff = 0.3.
  Keep only CoS with cos_npc >= 0.7. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.3.
  Show all CoSs to uncolocalized outcomes.
  Extracting colocalization results with cos_npc_cutoff = 0.9 and npc_outcome_cutoff = 0.5.
  Keep only CoS with cos_npc >= 0.9. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
  Show all CoSs to uncolocalized outcomes.
  Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 0.5.
  Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 1.
  Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 1.
  Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
  Keep only uCoS with pvalue of variants for the outcome < 1e-05.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  No uncolocalized results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 2 converged after 62 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  Validating input data.
  Validating input data.
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001.
  For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
  Validating input data.
  Starting gradient boosting algorithm.
  Running ColocBoost with assumption of one causal per outcome per region!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001.
  For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Using multiple testing correction method: lfdr. Outcome 2 for all variants are greater than 1. Will not update it!
  Performing inference on colocalization events.
  No colocalization results in this region!
  Validating input data.
  Starting gradient boosting algorithm.
  Performing inference on colocalization events.
  No colocalization results in this region!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  There is no colocalization in this region!. Showing margianl for all outcomes!
  Validating input data.
  Starting gradient boosting algorithm.
  Gradient boosting for outcome 3 converged after 9 iterations!
  Performing inference on colocalization events.
  Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
  Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
  No uncolocalized results in this region!
  [ FAIL 1 | WARN 2 | SKIP 2 | PASS 718 ]
  
  ══ Skipped tests (2) ═══════════════════════════════════════════════════════════
  • No ucos detected (1): 'test_Xref.R:593:3'
  • No ucos detected in test data (1): 'test_inference.R:1038:3'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Failure ('test_inference.R:1335:7'): get_robust_ucos and get_ucos_evidence work together ──
  Expected `all(evidence$npc_outcome >= 0.2 | evidence$npc_outcome == 0)` to be TRUE.
  Differences:
  `actual`:   FALSE
  `expected`: TRUE 
  
  
  [ FAIL 1 | WARN 2 | SKIP 2 | PASS 718 ]
  Error:
  ! Test failures.
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [67s/70s] OK
* checking PDF version of manual ... OK
* checking HTML version of manual ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 1 ERROR
See
  ‘/Users/ripley/R/packages/tests-devel/colocboost.Rcheck/00check.log’
for details.

      142.30 real       124.15 user        11.82 sys