Simulate plasma caffeine concentrations using population pharmacokinetic model described in Lee, Kim, Perera, McLachlan and Bae (2015) <doi:10.1007/s00431-015-2581-x>.

Provides rank-based compositional differential abundance analysis for microbiome count data with zero cells using the compositional accelerated failure time model of Satten, Li and Zhao (2025) "CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells" <doi:10.1101/2025.11.26.690468>. Zero counts are treated as censored observations below sample-specific detection limits, avoiding the use of pseudocounts. The package implements estimation and hypothesis testing procedures for assessing associations between microbial taxa and covariates while accounting for the compositional structure of sequencing count data. It supports taxon-level differential abundance analysis, estimation of regression effects under censoring induced by detection limits, and inference based on rank-based methods that remain applicable in the presence of excess zeros. Functions are provided for model fitting, significance testing, extraction of effect estimates, and summarization of results across taxa. The package also provides optional bootstrap calibration of taxon-level p-values for sensitivity analysis in small-taxon settings.

A time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

Provides functions for the estimation of the conditional Akaike
    information in generalized mixed-effect models fitted with (g)lmer() 
    from 'lme4', lme() from 'nlme' and gamm() from 'mgcv'.
    For a manual on how to use 'cAIC4', see Saefken et al. (2021) <doi:10.18637/jss.v099.i08>.

Allows to plot a number of information related to the interpretation of Correspondence Analysis' results. It provides the facility to plot the contribution of rows and columns categories to the principal dimensions, the quality of points display on selected dimensions, the correlation of row and column categories to selected dimensions, etc. It also allows to assess which dimension(s) is important for the data structure interpretation by means of different statistics and tests. The package also offers the facility to plot the permuted distribution of the table total inertia as well as of the inertia accounted for by pairs of selected dimensions. Different facilities are also provided that aim to produce interpretation-oriented scatterplots. Reference: Alberti 2015 <doi:10.1016/j.softx.2015.07.001>.