This package will be useful to anybody who wants to infer graphical models for all sorts of compositional data, though primarily intended for microbiome relative abundance data (generated from 16S ...
Biogeography has traditionally focused on the distribution of species, while community ecology has sought to explain the patterns of community composition. Species interactions networks have rarely ...
Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly ...
Joint Species Distribution Models (JSDM) have received a lot of attention in the last decade as they provide a general multivariate framework to study the joint abundances of all species from a ...
The model equations are as follows. $$ \begin{align*} \dfrac{\mathrm dS}{\mathrm dt} &= -\frac{\beta c S I}{N}, \\ \dfrac{\mathrm dI}{\mathrm dt} &= \frac{\beta c S I ...
The random nature of gene expression is well established experimentally. Mathematical modeling provides a means of understanding the factors leading to the observed stochasticity. In this article, we ...
The ability to predict an individual’s potential response to treatment would permit clinicians to more prudently allocate resources that support cognitive behavioral therapy for obsessive–compulsive ...
We have previously discussed the importance of estimating uncertainty in our measurements and incorporating it into data analysis 1. To know the extent to which we can generalize our observations, we ...
Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric ...
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