From e132195d6cb8c11ba2fcb5364c24d3a8043664d6 Mon Sep 17 00:00:00 2001 From: Glenn Johnson Date: Mon, 18 Jan 2021 12:54:06 -0600 Subject: [PATCH] add version 3.12.0 to r-mice (#21116) --- .../repos/builtin/packages/r-mice/package.py | 39 +++++++++++-------- 1 file changed, 22 insertions(+), 17 deletions(-) diff --git a/var/spack/repos/builtin/packages/r-mice/package.py b/var/spack/repos/builtin/packages/r-mice/package.py index afa9bb119d..b667bdaf12 100644 --- a/var/spack/repos/builtin/packages/r-mice/package.py +++ b/var/spack/repos/builtin/packages/r-mice/package.py @@ -7,23 +7,25 @@ class RMice(RPackage): - """Multiple imputation using Fully Conditional Specification (FCS) - implemented by the MICE algorithm as described in Van Buuren and - Groothuis-Oudshoorn (2011) . + """Multivariate Imputation by Chained Equations - Each variable has its own imputation model. Built-in imputation models are - provided for continuous data (predictive mean matching, normal), binary - data (logistic regression), unordered categorical data (polytomous logistic - regression) and ordered categorical data (proportional odds). MICE can - also impute continuous two-level data (normal model, pan, second-level - variables). Passive imputation can be used to maintain consistency between - variables. Various diagnostic plots are available to inspect the quality - of the imputations.""" + Multiple imputation using Fully Conditional Specification (FCS) implemented + by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn + (2011) . Each variable has its own imputation + model. Built-in imputation models are provided for continuous data + (predictive mean matching, normal), binary data (logistic regression), + unordered categorical data (polytomous logistic regression) and ordered + categorical data (proportional odds). MICE can also impute continuous + two-level data (normal model, pan, second-level variables). Passive + imputation can be used to maintain consistency between variables. Various + diagnostic plots are available to inspect the quality of the + imputations.""" homepage = "https://cloud.r-project.org/package=mice" url = "https://cloud.r-project.org/src/contrib/mice_3.0.0.tar.gz" list_url = "https://cloud.r-project.org/src/contrib/Archive/mice" + version('3.12.0', sha256='575d9e650d5fc8cd66c0b5a2f1e659605052b26d61f772fff5eed81b414ef144') version('3.6.0', sha256='7bc72bdb631bc9f67d8f76ffb48a7bb275228d861075e20c24c09c736bebec5d') version('3.5.0', sha256='4fccecdf9e8d8f9f63558597bfbbf054a873b2d0b0820ceefa7b6911066b9e45') version('3.0.0', sha256='98b6bb1c5f8fb099bd0024779da8c865146edb25219cc0c9542a8254152c0add') @@ -31,11 +33,14 @@ class RMice(RPackage): depends_on('r@2.10.0:', type=('build', 'run')) depends_on('r-broom', type=('build', 'run')) depends_on('r-dplyr', type=('build', 'run')) - depends_on('r-mass', type=('build', 'run')) - depends_on('r-mitml', type=('build', 'run')) - depends_on('r-nnet', type=('build', 'run')) + depends_on('r-generics', when='@3.12.0:', type=('build', 'run')) + depends_on('r-lattice', type=('build', 'run')) depends_on('r-rcpp', type=('build', 'run')) depends_on('r-rlang', type=('build', 'run')) - depends_on('r-rpart', type=('build', 'run')) - depends_on('r-survival', type=('build', 'run')) - depends_on('r-lattice', type=('build', 'run')) + depends_on('r-tidyr', when='@3.12.0:', type=('build', 'run')) + depends_on('r-cpp11', when='@3.12.0:', type=('build', 'run')) + depends_on('r-mitml', when='@:3.6.0', type=('build', 'run')) + depends_on('r-nnet', when='@:3.6.0', type=('build', 'run')) + depends_on('r-rpart', when='@:3.6.0', type=('build', 'run')) + depends_on('r-survival', when='@:3.6.0', type=('build', 'run')) + depends_on('r-mass', when='@:3.6.0', type=('build', 'run'))