From e132195d6cb8c11ba2fcb5364c24d3a8043664d6 Mon Sep 17 00:00:00 2001
From: Glenn Johnson <glenn-johnson@uiowa.edu>
Date: Mon, 18 Jan 2021 12:54:06 -0600
Subject: add version 3.12.0 to r-mice (#21116)
---
var/spack/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 @@ from spack import *
class RMice(RPackage):
- """Multiple imputation using Fully Conditional Specification (FCS)
- implemented by the MICE algorithm as described in Van Buuren and
- Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>.
+ """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) <doi:10.18637/jss.v045.i03>. 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'))
--
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