gCoda: Conditional Dependence Network Inference for Compositional Data

Conditional Dependence in Longitudinal Data Analysis

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian...

CCLasso: correlation inference for compositional data through Lasso

MOTIVATION Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study. Traditional Pearson correlation analysis t...

Exploiting Functional Dependence in Bayesian Network Inference

In this paper we propose an efficient method for Bayesian network inference in models with functional dependence. We generalize the multiplicative factorization method originally designed by Takikawa and D’Ambrosio (1999) for models with independence of causal influence. Using a hidden variable, we transform a probability potential into a product of two-dimensional potentials. The multiplicativ...

Inference on Conditional Quantile Residual Life for Censored Survival Data

Bridging conditional and marginal inference for spatially referenced binary data.

Spatially referenced binary data are common in epidemiology and public health. Owing to its elegant log-odds interpretation of the regression coefficients, a natural model for these data is logistic regression. To account for missing confounding variables that might exhibit a spatial pattern (say, socioeconomic, biological, or environmental conditions), it is customary to include a Gaussian spa...