CRAN Package Check Results for Package tidyseurat

Last updated on 2021-02-13 04:47:46 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.1.17 26.23 222.18 248.41 WARN
r-devel-linux-x86_64-debian-gcc 0.1.17 23.53 163.97 187.50 WARN
r-devel-linux-x86_64-fedora-clang 0.1.17 302.89 WARN
r-devel-linux-x86_64-fedora-gcc 0.1.17 273.72 WARN
r-devel-windows-ix86+x86_64 0.1.17 65.00 200.00 265.00 WARN
r-patched-linux-x86_64 0.1.17 18.17 209.16 227.33 WARN
r-patched-solaris-x86 0.1.17 429.30 WARN
r-release-linux-x86_64 0.1.17 17.39 212.42 229.81 WARN
r-release-macos-x86_64 0.1.17 OK
r-release-windows-ix86+x86_64 0.1.17 64.00 274.00 338.00 WARN
r-oldrel-macos-x86_64 0.1.17 ERROR

Check Details

Version: 0.1.17
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building 'figures_article.Rmd' using knitr
    --- finished re-building 'figures_article.Rmd'
    
    --- re-building 'introduction.Rmd' using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'magrittr'
    
    The following object is masked from 'package:purrr':
    
     set_names
    
    The following object is masked from 'package:tidyr':
    
     extract
    
    Attaching SeuratObject
    
    Attaching package: 'tidyseurat'
    
    The following object is masked from 'package:SeuratObject':
    
     pbmc_small
    
    The following object is masked from 'package:magrittr':
    
     extract
    
    The following objects are masked from 'package:dplyr':
    
     bind_cols, bind_rows, count
    
    The following object is masked from 'package:stats':
    
     filter
    
    `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
    tidyseurat says: A data frame is returned for independent data analysis.
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in irlba(A = t(x = object), nv = npcs, ...) :
     You're computing too large a percentage of total singular values, use a standard svd instead.
    tidyseurat says: A data frame is returned for independent data analysis.
    Calculating cluster 0
    For a more efficient implementation of the Wilcoxon Rank Sum Test,
    (default method for FindMarkers) please install the limma package
    --------------------------------------------
    install.packages('BiocManager')
    BiocManager::install('limma')
    --------------------------------------------
    After installation of limma, Seurat will automatically use the more
    efficient implementation (no further action necessary).
    This message will be shown once per session
    Calculating cluster 1
    Calculating cluster 2
    Calculating cluster 3
    Quitting from lines 219-232 (./../man/fragments/intro.Rmd)
    Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
    Error: processing vignette 'introduction.Rmd' failed with diagnostics:
    Problem with `filter()` input `..1`.
    x object 'avg_logFC' not found
    i Input `..1` is `top_n_rank(10, avg_logFC)`.
    i The error occurred in group 1: cluster = "0".
    --- failed re-building 'introduction.Rmd'
    
    SUMMARY: processing the following file failed:
     'introduction.Rmd'
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.1.17
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘figures_article.Rmd’ using knitr
    --- finished re-building ‘figures_article.Rmd’
    
    --- re-building ‘introduction.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'magrittr'
    
    The following object is masked from 'package:purrr':
    
     set_names
    
    The following object is masked from 'package:tidyr':
    
     extract
    
    Attaching SeuratObject
    
    Attaching package: 'tidyseurat'
    
    The following object is masked from 'package:SeuratObject':
    
     pbmc_small
    
    The following object is masked from 'package:magrittr':
    
     extract
    
    The following objects are masked from 'package:dplyr':
    
     bind_cols, bind_rows, count
    
    The following object is masked from 'package:stats':
    
     filter
    
    `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
    tidyseurat says: A data frame is returned for independent data analysis.
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in irlba(A = t(x = object), nv = npcs, ...) :
     You're computing too large a percentage of total singular values, use a standard svd instead.
    tidyseurat says: A data frame is returned for independent data analysis.
    Calculating cluster 0
    For a more efficient implementation of the Wilcoxon Rank Sum Test,
    (default method for FindMarkers) please install the limma package
    --------------------------------------------
    install.packages('BiocManager')
    BiocManager::install('limma')
    --------------------------------------------
    After installation of limma, Seurat will automatically use the more
    efficient implementation (no further action necessary).
    This message will be shown once per session
    Calculating cluster 1
    Calculating cluster 2
    Calculating cluster 3
    Quitting from lines 219-232 (./../man/fragments/intro.Rmd)
    Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
    Error: processing vignette 'introduction.Rmd' failed with diagnostics:
    Problem with `filter()` input `..1`.
    ✖ object ‘avg_logFC’ not found
    ℹ Input `..1` is `top_n_rank(10, avg_logFC)`.
    ℹ The error occurred in group 1: cluster = "0".
    --- failed re-building ‘introduction.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘introduction.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64

Version: 0.1.17
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘figures_article.Rmd’ using knitr
    --- finished re-building ‘figures_article.Rmd’
    
    --- re-building ‘introduction.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'magrittr'
    
    The following object is masked from 'package:purrr':
    
     set_names
    
    The following object is masked from 'package:tidyr':
    
     extract
    
    Attaching SeuratObject
    
    Attaching package: 'tidyseurat'
    
    The following object is masked from 'package:SeuratObject':
    
     pbmc_small
    
    The following object is masked from 'package:magrittr':
    
     extract
    
    The following objects are masked from 'package:dplyr':
    
     bind_cols, bind_rows, count
    
    The following object is masked from 'package:stats':
    
     filter
    
    `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
    tidyseurat says: A data frame is returned for independent data analysis.
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in irlba(A = t(x = object), nv = npcs, ...) :
     You're computing too large a percentage of total singular values, use a standard svd instead.
    tidyseurat says: A data frame is returned for independent data analysis.
    Calculating cluster 0
    For a more efficient implementation of the Wilcoxon Rank Sum Test,
    (default method for FindMarkers) please install the limma package
    --------------------------------------------
    install.packages('BiocManager')
    BiocManager::install('limma')
    --------------------------------------------
    After installation of limma, Seurat will automatically use the more
    efficient implementation (no further action necessary).
    This message will be shown once per session
    Calculating cluster 1
    Calculating cluster 2
    Calculating cluster 3
    Quitting from lines 219-232 (./../man/fragments/intro.Rmd)
    Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
    Error: processing vignette 'introduction.Rmd' failed with diagnostics:
    Problem with `filter()` input `..1`.
    ✖ object 'avg_logFC' not found
    ℹ Input `..1` is `top_n_rank(10, avg_logFC)`.
    ℹ The error occurred in group 1: cluster = "0".
    --- failed re-building ‘introduction.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘introduction.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 0.1.17
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘figures_article.Rmd’ using knitr
    --- finished re-building ‘figures_article.Rmd’
    
    --- re-building ‘introduction.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'magrittr'
    
    The following object is masked from 'package:purrr':
    
     set_names
    
    The following object is masked from 'package:tidyr':
    
     extract
    
    Attaching SeuratObject
    
    Attaching package: 'tidyseurat'
    
    The following object is masked from 'package:SeuratObject':
    
     pbmc_small
    
    The following object is masked from 'package:magrittr':
    
     extract
    
    The following objects are masked from 'package:dplyr':
    
     bind_cols, bind_rows, count
    
    The following object is masked from 'package:stats':
    
     filter
    
    `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
    tidyseurat says: A data frame is returned for independent data analysis.
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in irlba(A = t(x = object), nv = npcs, ...) :
     You're computing too large a percentage of total singular values, use a standard svd instead.
    tidyseurat says: A data frame is returned for independent data analysis.
    Calculating cluster 0
    Calculating cluster 1
    Calculating cluster 2
    Calculating cluster 3
    Quitting from lines 219-232 (./../man/fragments/intro.Rmd)
    Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
    Error: processing vignette 'introduction.Rmd' failed with diagnostics:
    Problem with `filter()` input `..1`.
    ✖ object 'avg_logFC' not found
    ℹ Input `..1` is `top_n_rank(10, avg_logFC)`.
    ℹ The error occurred in group 1: cluster = "0".
    --- failed re-building ‘introduction.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘introduction.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.17
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building 'figures_article.Rmd' using knitr
    --- finished re-building 'figures_article.Rmd'
    
    --- re-building 'introduction.Rmd' using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    
    Attaching package: 'magrittr'
    
    The following object is masked from 'package:purrr':
    
     set_names
    
    The following object is masked from 'package:tidyr':
    
     extract
    
    Attaching SeuratObject
    
    Attaching package: 'tidyseurat'
    
    The following object is masked from 'package:SeuratObject':
    
     pbmc_small
    
    The following object is masked from 'package:magrittr':
    
     extract
    
    The following objects are masked from 'package:dplyr':
    
     bind_cols, bind_rows, count
    
    The following object is masked from 'package:stats':
    
     filter
    
    `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
    tidyseurat says: A data frame is returned for independent data analysis.
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
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    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in sqrt(1/i) : NaNs produced
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in theta.ml(y = y, mu = fit$fitted) : iteration limit reached
    Warning in irlba(A = t(x = object), nv = npcs, ...) :
     You're computing too large a percentage of total singular values, use a standard svd instead.
    tidyseurat says: A data frame is returned for independent data analysis.
    Calculating cluster 0
    For a more efficient implementation of the Wilcoxon Rank Sum Test,
    (default method for FindMarkers) please install the limma package
    --------------------------------------------
    install.packages('BiocManager')
    BiocManager::install('limma')
    --------------------------------------------
    After installation of limma, Seurat will automatically use the more
    efficient implementation (no further action necessary).
    This message will be shown once per session
    Calculating cluster 1
    Calculating cluster 2
    Calculating cluster 3
    Quitting from lines 219-232 (./../man/fragments/intro.Rmd)
    Quitting from lines 50-51 (./../man/fragments/intro.Rmd)
    Error: processing vignette 'introduction.Rmd' failed with diagnostics:
    Problem with `filter()` input `..1`.
    x object 'avg_logFC' not found
    i Input `..1` is `top_n_rank(10, avg_logFC)`.
    i The error occurred in group 1: cluster = "0".
    --- failed re-building 'introduction.Rmd'
    
    SUMMARY: processing the following file failed:
     'introduction.Rmd'
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64

Version: 0.1.17
Check: whether package can be installed
Result: ERROR
    Installation failed.
Flavor: r-oldrel-macos-x86_64