R-miss-tastic

Package:

naniar

Authors:

Nicholas Tierney [aut, cre], Di Cook [aut], Miles McBain [aut], Colin Fay [aut], Mitchell O’Hara-Wild [ctb], Jim Hester [ctb], Luke Smith [ctb], Andrew Heiss [ctb]

Category:

Data Structures, Summaries, and Visualisations for Missing Data

Use-Cases:

Visualization of missing values, descriptive statistics, …

Popularity:

Description:

Missing values are ubiquitous in data and need to be carefully explored and handled in the initial stages of analysis. In this vignette we describe the tools in the package naniar for exploring missing data structures with minimal deviation from the common workflows of ggplot and tidy data.

Package:

imputomics

Authors:

author: Michał Burdukiewicz, Krystyna Grzesiak, Jarosław Chilimoniuk, Jakub Kołodziejczyk, Dominik Nowakowski

Category:

Single and Multiple Imputation, Metabolomics, Left-Censored Missing Data

Use-Cases:

Imputation for ‘omics’ data, Imputation for left-censored data.

Description:

A robust wrapper package containing a range of methods for simulating and imputing missing values in different types of omics data such as genomics, transcriptomics, proteomics, and metabolomics. Provides tools for comparing and evaluating the performance of imputation methods and a web server.