Package: gerbil 0.1.9

gerbil: Generalized Efficient Regression-Based Imputation with Latent Processes

Implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) <arxiv:2008.02243>.

Authors:Michael Robbins [aut, cre], Max Griswold [ctb], Pedro Nascimento de Lima [ctb]

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gerbil.pdf |gerbil.html✨
gerbil/json (API)
NEWS

# Install 'gerbil' in R:
install.packages('gerbil', repos = c('https://michaelwrobbins.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ihd - Example data from the India Human Development Survey
  • ihd_mar - Missing at Random example data from the India Human Development Survey
  • ihd_mcar - Missing Completely at Random example data from the India Human Development Survey

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 3 scripts 178 downloads 5 exports 50 dependencies

Last updated 2 years agofrom:2152129d08. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 31 2024
R-4.5-winOKOct 31 2024
R-4.5-linuxOKOct 31 2024
R-4.4-winOKOct 31 2024
R-4.4-macOKOct 31 2024
R-4.3-winOKOct 31 2024
R-4.3-macOKOct 31 2024

Exports:cor_gerbilgerbilgof_gerbilimputedwrite.gerbil

Dependencies:askpassbootcellrangerclassclicpp11crayoncurldata.tableDescToolse1071ExactexpmfansigldgluehmshttrjsonlitelatticelifecyclelmommagrittrMASSMatrixmimemvtnormopensslopenxlsxpbapplypillarpkgconfigprettyunitsprogressproxyR6RcppreadxlrematchrlangrootSolverstudioapistringisystibbletruncnormutf8vctrswithrzip

Gerbil Introduction

Rendered fromgerbil_introduction_vignette.Rnwusingutils::Sweaveon Oct 31 2024.

Last update: 2021-03-23
Started: 2021-03-23

Readme and manuals

Help Manual

Help pageTopics
Correlation Analysis for 'gerbil' Objectscor_gerbil
General Efficient Regression-Based Imputation with Latent processesgerbil
Goodness-of-fit testing for 'gerbil' objectsgof_gerbil
Example data from the India Human Development Surveyihd
Missing at Random example data from the India Human Development Surveyihd_mar
Missing Completely at Random example data from the India Human Development Surveyihd_mcar
Extracting imputed datasets from gerbil objectsimputed
Plotting for gerbil objectsplot.gerbil
Prints a 'cor_gerbil' object. Printed output includes the average difference of correlations, as well as summaries of the test statistics based on Fisher's z and their p-values.print.cor_gerbil
Prints a 'gerbil' object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.print.gerbil
Prints a 'gof_gerbil' object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs.print.gof_gerbil
Summarises a 'gerbil' object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.summary.cor_gerbil
Summarises a 'gerbil' object. Printed output includes a variable-by-variable summary of variable types and missingness rates. The implemented predictor matrix is also provided.summary.gerbil
Summarises a 'gerbil' object. Printed output pertains to the goodness-of-fit tests that are applied in order to compare the distribution between observed and imputed cases for relevant variables or variable pairs.summary.gof_gerbil
Write imputed datasets from gerbil objects to a file or fileswrite.gerbil