Expands 'iNEXT' to include the estimation of sample completeness and evenness. The package provides simple functions to perform the following four-step biodiversity analysis:
       STEP 1: Assessment of sample completeness profiles.
       STEP 2a: Analysis of size-based rarefaction and extrapolation sampling curves to 
       determine whether the asymptotic diversity can be accurately estimated. 
       STEP 2b: Comparison of the observed and the estimated asymptotic diversity profiles. 
       STEP 3: Analysis of non-asymptotic coverage-based rarefaction and extrapolation sampling curves.
       STEP 4: Assessment of evenness profiles. 
       The analyses in STEPs 2a, 2b and STEP 3 are mainly based on the previous 'iNEXT' package. Refer to the 'iNEXT' package for details. This package is mainly focusing on the computation for STEPs 1 and 4. See Chao et al. (2020) <doi:10.1111/1440-1703.12102> for statistical background.

As a sequel to 'iNEXT', the 'iNEXT.beta3D' package provides functions to compute 
              standardized taxonomic, phylogenetic, and functional diversity (3D) estimates 
              with a common sample size (for alpha and gamma diversity) or sample coverage 
              (for alpha, beta, gamma diversity as well as dissimilarity or turnover indices). 
              Hill numbers and their generalizations are used to quantify 3D and to make 
              multiplicative decomposition (gamma = alpha x beta). The package also features 
              size- and coverage-based rarefaction and extrapolation sampling curves to 
              facilitate rigorous comparison of beta diversity across datasets.  
              See Chao et al. (2023) <doi:10.1002/ecm.1588> for more details.

The objective of this package is to perform inference using
    an expressive statistical grammar that coheres with the tidy design
    framework.

A method for inferring cell-specific gene regulatory network from single-cell sequencing data.

Functions to make inference about the 
        standardized mortality ratio (SMR) when evaluating the
        effect of a screening program. The package is
        based on methods described in Sasieni (2003) 
        <doi: 10.1097/00001648-200301000-00026> and 
        Talbot et al. (2011) <doi: 10.1002/sim.4334>.