Showing 5 of total 5 results (show query)
njtierney
naniar:Data Structures, Summaries, and Visualisations for Missing Data
Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data. The work is fully discussed at Tierney & Cook (2023) <doi:10.18637/jss.v105.i07>.
Maintained by Nicholas Tierney. Last updated 19 days ago.
data-visualisationggplot2missing-datamissingnesstidy-data
657 stars 15.63 score 5.1k scripts 9 dependents
ropensci
visdat:Preliminary Visualisation of Data
Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
Maintained by Nicholas Tierney. Last updated 9 months ago.
exploratory-data-analysismissingnesspeer-reviewedropenscivisualisation
452 stars 13.31 score 2.1k scripts 11 dependents
tirgit
missCompare:Intuitive Missing Data Imputation Framework
Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.
Maintained by Tibor V. Varga. Last updated 4 years ago.
comparisoncomparison-benchmarksimputationimputation-algorithmimputation-methodsimputationskolmogorov-smirnovmissingmissing-datamissing-data-imputationmissing-status-checkmissing-valuesmissingnesspost-imputation-diagnosticsrmse
39 stars 5.89 score 40 scripts
nelson-gon
mde:Missing Data Explorer
Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data lies and therefore find the most appropriate action to take. Graham WJ (2009) <doi:10.1146/annurev.psych.58.110405.085530>.
Maintained by Nelson Gonzabato. Last updated 3 years ago.
data-analysisdata-cleaningdata-explorationdata-sciencedatacleanerdatacleaningexploratory-data-analysismissingmissing-datamissing-value-treatmentmissing-valuesmissingnessomitrecodereplacestatistics
4 stars 5.61 score 34 scripts
moseleybioinformaticslab
ICIKendallTau:Calculates information-content-informed Kendall-tau
Provides functions for calculating information-content-informed Kendall-tau. This version of Kendall-tau allows for the inclusion of missing values.
Maintained by Robert M Flight. Last updated 5 months ago.
6 stars 4.56 score 15 scripts