zCompositions — R package for multivariate imputation of left-censored data under a compositional approach

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Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “Fortunately, there are acceptable methods of dealing with 0 count values as both point estimates using zCompositions R package (Palarea-Albaladejo and Martín-Fernández, 2015), and as a probability distribution using ALDEx2 available on Bioconductor.”
Normalization and microbial differential abundance strategies depend upon data characteristics (2017, background): “Instead, one may consider standard zero imputation methods [69].”
NetCoMi: network construction and comparison for microbiome data in R (2020, neutral): “The three approaches are implemented in R via the zCompositions package [37].”
Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal (2022, methodology): “Using alternative imputation strategies [73] did not change the essential results to be presented here.”
A Novel Sparse Compositional Technique Reveals Microbial Perturbations (2019, methodology): “It is important to note that while there have been previous methods that handle zeros in compositional data sets, such as methods available in zCompositions (38), the methodology here is fundamentally different.”