From a8f38fe71d947b0ab29376f03c3740fb716d2b02 Mon Sep 17 00:00:00 2001 From: Martin Date: Thu, 16 Jan 2025 10:43:59 +0100 Subject: [PATCH] repo -> repository --- DESCRIPTION | 2 +- cran-comments.md | 22 +++++++++------------- man/shapr-package.Rd | 2 +- 3 files changed, 11 insertions(+), 15 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 2e926792..8b79e58f 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -7,7 +7,7 @@ Description: Complex machine learning models are often hard to interpret. Howeve with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. - An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repo. + An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository. Authors@R: c( person("Martin", "Jullum", email = "Martin.Jullum@nr.no", role = c("cre", "aut"), comment = c(ORCID = "0000-0003-3908-5155")), person("Lars Henry Berge", "Olsen", email = "lhbolsen@nr.no", role = "aut", comment = c(ORCID = "0009-0006-9360-6993")), diff --git a/cran-comments.md b/cran-comments.md index 35184ecd..6b0997ae 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,5 +1,9 @@ # shapr 1.0.1 (Major release) +* **By CRAN request** after initial submission: + * Fixed spelling in DESCRIPTION + * Reduced tarball size mainly by (temporary) removing snapshot files from the build as they are not run on CRAN. + * Complete rewrite of the package compared to the previous CRAN release. We moved from two main user functions `shapr()` and `explain()` to a single function `explain()` that includes both. Thus, this change breaks essentially all existing code that uses the previous version of the package. @@ -23,24 +27,16 @@ The win-builder and R-hub tests are run without snapshots tests (to replicate CR ## R CMD check results -There were no ERRORs or WARNINGs - -There were 2 NOTES - -### NOTE 1 (on win-builder (oldrelease)): - -Possibly misspelled words in DESCRIPTION: - shaprpy (10:35) - -> This refers to the Python wrapper of the package and is not misspelled. +There were no ERRORs, WARNINGs +There were 1 NOTE -### NOTE 2 (multiple platforms): +### NOTE (multiple platforms): * checking installed package size ... NOTE - installed size is 8.0Mb + installed size is 7.1Mb sub-directories of 1Mb or more: - doc 4.4Mb + doc 4.2Mb libs 1.3Mb > The package is growing in size, uses more complied code, and the documentation is comprehensive. diff --git a/man/shapr-package.Rd b/man/shapr-package.Rd index 051940d2..f1671bdb 100644 --- a/man/shapr-package.Rd +++ b/man/shapr-package.Rd @@ -6,7 +6,7 @@ \alias{shapr-package} \title{shapr: Prediction Explanation with Dependence-Aware Shapley Values} \description{ -Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repo. +Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository. } \seealso{ Useful links: