Count Regression for Correlated Observations with the Beta-Binomial
Maximum Likelihood for the Beta-binomial Distribution
Check for nested models
Rename taxa
Identify differentially-abundant and differentially-variable taxa usin...
Function to subset and convert phyloseq data
Corncob package documentation.
Hyperbolic cotangent transformation
Betabinomial density
Negative betabinomial density
Densities of beta binomial distributions, permitting non integer x and...
Identify differentially-abundant and differentially-variable taxa
Function to run a bootstrap iteration
Fisher's z transformation
Generate initialization for optimization
Get index of restricted terms for Wald test
Parameter Gradient Vector
Get highest density interval of beta-binomial
Compute Hessian matrix at the MLE
Inverse Fisher's z transformation
Inverse logit transformation
Logit transformation
Likelihood ratio test
Objective function
Transform OTUs to their taxonomic label
Parametric bootstrap likelihood ratio test
Parametric bootstrap Rao test
Parametric bootstrap Wald test
Pipe operator
Plotting function
differentialTest plot function
Print function
differentialTest print function
Print summary function
Get quantiles of beta binom
Rao-type chi-squared test (model-based or robust)
Compute sandwich estimate of variance-covariance matrix
Compute sandwich standard errors. Legacy function. Use sand_vcov inste...
Compute score at the MLE
Simulate from beta-binomial model
Summary function
Wald-type chi-squared test
Wald-type chi-squared test statistic (model-based or robust)
Wald-type t test (model-based or robust)
Function to throw error if the phyloseq
package is called but it is ...
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
Useful links