Description: fit a couple-based joint latent class model with an interaction between a couple(e.g., female and male partners) and High-dimensional semicontinuous chemical biomarker for each partner of the couple. This formulation introduces a dependence structure between the chemical patterns within a couple and between the chemical patterns and the risk of desease. A Bayesian framework examines the chemical biomarker profile from each member of the couple and the risk of disease. The complex chemical mixtures on each couple link to disease risk through unobserved latent classes. we posit that two sets of latent classes, each characterizing the chemical mixture patterns of one partner of the couple, are linked to the risk of disease through a logistic model with main and interaction effects between latent classes. The semicontinuous chimical biomarker viarables (1/4 zeros and right-skewed non-zero values) are processed through Tobit modeling framework. Markov chain Monte Carlo algorithms was used to obtain posterior estimates of model parameters.The user supplies data and priors, and a list of posterior estimates of model parameters is returned.