mice 3.14.0

Major changes

• Adds four new univariate functions using the lasso for automatic variable selection:

Function	Description
mice.impute.lasso.norm()	Lasso linear regression
mice.impute.lasso.logreg()	Lasso logistic regression
mice.impute.lasso.select.norm()	Lasso selector + linear regression
mice.impute.lasso.select.logreg()	Lasso selector + logistic regression

Contributed by @EdoardoCostantini (#438).

• Adds Jamshidian && Jalal's non-parametric MCAR test, mice::MCAR() and associated plot method. Contributed by @cjvanlissa (#423).

• Adds two new functions pool.syn() and pool.scalar.syn() that specialise pooling estimates from synthetic data. The "reiter2003" pooling rule assumes that synthetic data were created from complete data. Thanks Thom Volker (#436).

• Avoids changing the global .Random.seed (#426, #432) by implementing withr::local_preserve_seed() and withr::local_seed(). This change provides stabler behavior in complex scripts. The change does not appear to break reproducibility when mice() was run with a seed. Nevertheless, if you run into a reproducibility problem, install mice 3.13.12 or before.

• Improves the imputation of parabolic data in mice.impute.quadratic(), adds a parameter quad.outcome containing the name of the outcome variable in the complete-data model. Contributed @Mingyang-Cai, @gerkovink (#408)

• By default, mice.impute.rf() now uses the faster ranger package as back-end instead of randomForest package. If you want the old behaviour specify the rfPackage = "randomForest" argument to the mice(...) call. Contributed @prockenschaub (#431).

• Generalises pool() so that it processes the parameters from all gamlss sub-models. Thanks Marcio Augusto Diniz (#406, #405)

• Uses the robust standard error estimate for pooling when pool() can extract robust.se from the object returned by broom::tidy() (#310)

Bug fixes

• Contains an emergency solution as install.on.demand() broke the standard CRAN workflow. mice 3.14.0 does not call install.on.demand() anymore for recommended packages. Also, install.on.demand() will not run anymore in non-interactive mode.
• Repairs an error in the mice:::barnard.rubin() function for infinite dfcom. Thanks @huftis (#441).
• Solves problem with Xi <- as.matrix(...) in mice.impute.2l.lmer() that occurred when a cluster contains only one observation (#384)
• Edits the predictorMatrix to a monotone pattern if visitSequence = "monotone" and maxit = 1 (#316)
• Solves a problem with the plot produced by md.pattern() (#318, #323)
• Fixes the intercept in make.formulas() (#305, #324)
• Fixes seed when using newdata in mice.mids() (#313, #325)
• Solves a problem with row names of the where element created in rbind() (#319)
• Solves a bug in mnar imputation routine. Contributed by Margarita Moreno Betancur.

Minor changes

• Replaces URL to jstatsoft with DOI
• Update reference to literature (#442)
• Informs the user that pool() cannot take a mids object (#433)
• Updates documentation for post-processing functionality (#387)
• Adds Rcpp necessities
• Solves a problem with "last resort" initialisation of factors (#410)
• Documents the "flat-line behaviour" of mice.impute.2l.lmer() to indicate a problem in fitting the imputation model (#385)
• Add reprex to test (#326)
• Documents that multivariate imputation methods do not support the post parameter (#326)