Alternative implementation of the beautiful 'MissForest'
algorithm used to impute mixed-type data sets by chaining random
forests, introduced by Stekhoven, D.J. and Buehlmann, P. (2012)
<doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the
lightning fast random jungle package 'ranger'. Between the iterative
model fitting, we offer the option of using predictive mean matching.
This firstly avoids imputation with values not already present in the
original data (like a value 0.3334 in 0-1 coded variable).  Secondly,
predictive mean matching tries to raise the variance in the resulting
conditional distributions to a realistic level. This would allow e.g.
to do multiple imputation when repeating the call to missRanger().  A
formula interface allows to control which variables should be imputed
by which.
survival, dplyr, mice, rmarkdown, knitr, testthat

Handling Missing Data in Stochastic Block Models
c(
person("Julien", "Chiquet", role = c("aut", "cre"), email = "julien.chiquet@inra.fr",
comment = c(ORCID = "0000-0002-3629-3429")),
person("Pierre", "Barbillon", role = c("aut"), email = "pierre.barbillon@agroparistech.fr",
comment = c(ORCID = "0000-0002-7766-7693")),
person("Timothée", "Tabouy", role = c("aut"), email = "timothee.tabouy@agroparistech.fr")
)
When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0
due to missing information between node pairs), it is possible to account for the underlying process
that generates those NAs. 'missSBM' adjusts the popular stochastic block model from network data
sampled under various missing data conditions, as described in Tabouy, Barbillon and Chiquet (2019) <doi:10.1080/01621459.2018.1562934>.
Rcpp, methods, ape, igraph, nloptr, corrplot, R6, magrittr
'RcppExports.R' 'SBM-Class.R' 'SBM_fit-Class.R'
'SBM_fit_covariates-Class.R' 'SBM_fit_nocovariate-Class.R'
'SBM_sampler-Class.R' 'er_network.R' 'estimate.R'
'frenchblog2007.R' 'missSBM-package.R' 'utils_missSBM.R'
'networkSampling-Class.R' 'networkSampling_fit-Class.R'
'missSBM_fit-Class.R' 'missSBM_collection-Class.R'
'networkSampler-Class.R' 'prepare_data.R' 'sample.R'
'sampledNetwork-Class.R' 'simulate.R' 'utils-pipe.R'
'utils_initialization.R' 'war.R'
aricode, blockmodels, testthat, covr, knitr, rmarkdown,
ggplot2
Julien Chiquet [aut, cre] (<https://orcid.org/0000-0002-3629-3429>),
Pierre Barbillon [aut] (<https://orcid.org/0000-0002-7766-7693>),
Timothée Tabouy [aut]