Bias estimation and calibration 


Estimate bias from control measurements 

Calibrate a relativeabundance matrix by a bias vector 


Taxon cooccurrence network 
Compositionaldata computations 

Compositionally perturb a relativeabundance matrix 

Pairwise ratios of vector elements and matrix rows or columns 

Compute taxon ratios from a tidy data frame 

Compute the center (compositional mean) of a set of compositions 

Generate bootstrap replicates of the sample center 

Geometrically center the elements of x 

Close the elements of x to proportions 

Compute the centered logratio transform of x 

Logit (logodds) of the probability vector x 

Odds of the probability vector x 

Aitchison norm of x 

Distance or dissimilarity between relative abundance vectors x and y 

Geometric (multiplicative) version of the function f 

Geometric absolute value of x 

Geometric mean of x 

Geometric range of x 

Geometric standard deviation of x 

Dataframe manipulation and coercion 

Coerce a (wide) data frame to a matrix 

Create a matrix from columns of a tidy data frame 

Mutate within groups 