1- # =============================================================================
1+ # =============================================================================
22# Put together the package
3- # =============================================================================
3+ # =============================================================================
44
55# WORKFLOW: UPDATE EXISTING PACKAGE
66# 1) Modify package content and documentation.
@@ -15,15 +15,15 @@ library(usethis)
1515use_description(
1616 fields = list (
1717 Title = " Fast Imputation of Missing Values" ,
18- Version = " 2.6.1 " ,
19- Description = " Alternative implementation of the beautiful 'MissForest' algorithm used to impute
20- mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and
21- Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
22- lightning fast random forest package 'ranger'. Between the iterative model fitting,
23- we offer the option of using predictive mean matching. This firstly avoids imputation
24- with values not already present in the original data (like a value 0.3334 in 0-1 coded variable).
25- Secondly, predictive mean matching tries to raise the variance in the resulting conditional
26- distributions to a realistic level. This would allow, e.g., to do multiple imputation when
18+ Version = " 2.6.2 " ,
19+ Description = " Alternative implementation of the beautiful 'MissForest' algorithm used to impute
20+ mixed-type data sets by chaining random forests, introduced by Stekhoven, D.J. and
21+ Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
22+ lightning fast random forest package 'ranger'. Between the iterative model fitting,
23+ we offer the option of using predictive mean matching. This firstly avoids imputation
24+ with values not already present in the original data (like a value 0.3334 in 0-1 coded variable).
25+ Secondly, predictive mean matching tries to raise the variance in the resulting conditional
26+ distributions to a realistic level. This would allow, e.g., to do multiple imputation when
2727 repeating the call to missRanger(). Out-of-sample application is supported as well." ,
2828 `Authors@R` = " person('Michael', 'Mayer', email = 'mayermichael79@gmail.com', role = c('aut', 'cre'))" ,
2929 Depends = " R (>= 3.5.0)" ,
@@ -42,8 +42,10 @@ use_gpl_license(2)
4242use_github_links() # use this if this project is on github
4343
4444# Your files that do not belong to the package itself (others are added by "use_* function")
45- use_build_ignore(c(" ^packaging.R$" , " [.]Rproj$" , " ^backlog$" ,
46- " ^cran-comments.md$" , " ^logo.png$" ), escape = FALSE )
45+ use_build_ignore(c(
46+ " ^packaging.R$" , " [.]Rproj$" , " ^backlog$" ,
47+ " ^cran-comments.md$" , " ^logo.png$"
48+ ), escape = FALSE )
4749
4850# If your code uses the pipe operator %>%
4951# use_pipe()
@@ -81,9 +83,9 @@ use_github_action("pkgdown")
8183
8284use_revdep()
8385
84- # =============================================================================
86+ # =============================================================================
8587# Finish package building (can use fresh session)
86- # =============================================================================
88+ # =============================================================================
8789
8890library(devtools )
8991
@@ -98,7 +100,7 @@ install()
98100if (FALSE ) {
99101 check_win_devel()
100102 check_rhub()
101-
103+
102104 # Takes long # devtools::install_github("r-lib/revdepcheck")
103105 revdepcheck :: revdep_check(num_workers = 4L , bioc = FALSE )
104106
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