version ready to submit to CRAN

NEWS.md

@@ -2,11 +2,11 @@
 
 - removed dependency on reshape2
 - adjusting other dependencies
+- new theme to remove context from plots
+- updated roxygen dependency
 
 # nullabor 0.3.9
 
-- new theme to remove context from plots
-- updated roxygen dependency
 - CRAN fixes, minor code changes
 
 # nullabor 0.3.8

cran-comments.md

@@ -1,7 +1,24 @@
-# Addressing the requested fix from CRAN
+## Overview 
 
-This update to the package is to address submission problems identified at:
+These are small changes, including removing some dependencies. Also fixed issues at https://cran.rstudio.com//web/checks/check_results_nullabor.html. 
 
-https://win-builder.r-project.org/incoming_pretest/nullabor_0.3.8_20200212_055129/Windows/00check.log
+── R CMD check results ────────────────────── nullabor 0.3.10 ────
+Duration: 1m 24.9s
 
-https://win-builder.r-project.org/incoming_pretest/nullabor_0.3.8_20200212_055129/Debian/00check.log
+0 errors ✔ | 0 warnings ✔ | 0 notes ✔
+
+## Test environment
+
+* R version 4.4.1 (2024-06-14) -- "Race for Your Life"
+* https://win-builder.r-project.org/
+* Linux: 
+
+## Reverse dependencies
+
+Installing DEV version of nullabor
+── CHECK ─────────────────────────────────────────── 3 packages ──
+✔ agridat 1.23 ── E: 0 | W: 0 | N: 0 
+✔ metaviz 0.3.1 ── E: 0 | W: 0 | N: 0 
+✔ regressinator 0.1.3 ── E: 0 | W: 0 | N: 0 
+OK: 3 
+BROKEN: 0

vignettes/distances.Rmd

@@ -159,6 +159,5 @@ distplot(dist.vals)
 References
 ----------
 
-Roy Chowdhury, N. and Cook, D. and Hofmann, H. and Majumder, M. and Zhao, Y. (2014)
-Utilizing Distance Metrics on Lineups to Examine What People Read From Data Plots,
-http://arxiv.org/abs/1408.1889.
+Roy Chowdhury, N. and Cook, D. and Hofmann, H. and Majumder, M. (2018) Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data, Journal of Computational and Graphical Statistics, 27(1), 132–145,
+[https://doi.org/10.1080/10618600.2017.1356323](https://doi.org/10.1080/10618600.2017.1356323).

vignettes/nullabor.Rmd

@@ -140,16 +140,15 @@ visual_power(turk_results)
 References
 ----------
 
-Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F, Wickham, H. (2009) Statistical Inference for Exploratory Data Analysis and Model Diagnostics, Royal Society Philosophical
- Transactions A, 367:4361--4383.
+Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F, Wickham, H. (2009) Statistical Inference for Exploratory Data Analysis and Model Diagnostics, Royal Society Philosophical Transactions A, 367:4361--4383, [https://doi.org/10.1098/rsta.2009.0120](https://doi.org/10.1098/rsta.2009.0120).
 
-Wickham, H., Cook, D., Hofmann, H. and Buja, A. (2010) Graphical Inference for Infovis, IEEE Transactions on Visualization and Computer Graphics, 16(6):973--979, [http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161](http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161). Best paper award.
+Wickham, H., Cook, D., Hofmann, H. and Buja, A. (2010) Graphical Inference for Infovis, IEEE Transactions on Visualization and Computer Graphics, 16(6):973--979, [https://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161](https://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161). Best paper award.
 
-Hofmann, H., Follett, L., Majumder, M. and Cook, D. (2012) Graphical Tests for Power Comparison of Competing Designs, IEEE Transactions on Visualization and Computer Graphics, 18(12):2441--2448, [http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.230](http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.230).
+Hofmann, H., Follett, L., Majumder, M. and Cook, D. (2012) Graphical Tests for Power Comparison of Competing Designs, IEEE Transactions on Visualization and Computer Graphics, 18(12):2441--2448, [https://doi.ieeecomputersociety.org/10.1109/TVCG.2012.230](https://doi.ieeecomputersociety.org/10.1109/TVCG.2012.230).
 
-Majumder, M., Hofmann, H. and Cook, D. (2013) Validation of Visual Statistical Inference, Applied to Linear Models, Journal of the American Statistical Association, 108(503):942--956. Featured Article http://amstat.tandfonline.com/doi/pdf/10.1080/01621459.2013.808157.
+Majumder, M., Hofmann, H. and Cook, D. (2013) Validation of Visual Statistical Inference, Applied to Linear Models, Journal of the American Statistical Association, 108(503):942--956. Featured Article [https://doi.org/10.1080/01621459.2013.808157](https://doi.org/10.1080/01621459.2013.808157).
 
-Roy Chowdhury, N., Cook, D., Hofmann, H., Majumder, M., Lee, E. K., & Toth, A. L. (2014). Using visual statistical inference to better understand random class separations in high dimension, low sample size data. Computational Statistics, 30(2), 293-316. [10.1007/s00180-014-0534-x](http://dx.doi.org/10.1007/s00180-014-0534-x)
+Roy Chowdhury, N., Cook, D., Hofmann, H., Majumder, M., Lee, E. K., & Toth, A. L. (2014). Using visual statistical inference to better understand random class separations in high dimension, low sample size data. Computational Statistics, 30(2), 293-316. [https://doi.org/10.1007/s00180-014-0534-x](https://doi.org/10.1007/s00180-014-0534-x)
 
 Majumder, M. and Hofmann, H. and Cook, D. (2014) Human Factors Influencing Visual Statistical Inference,
-[http://arxiv.org/abs/1408.1974](http://arxiv.org/abs/1408.1974).
+[https://doi.org/10.48550/arXiv.1408.1974](https://doi.org/10.48550/arXiv.1408.1974).