- Referenced in 505 articles
- linear regression, logistic and multinomial regression models, poisson regression and the Cox model. Two recent...
- Referenced in 49 articles
- replicated count data. An overdispersed Poisson model is used to account for both biological...
- Referenced in 66 articles
- framework for two classes of simple model problems (Poisson and linearized elasticity) and discuss...
- Referenced in 25 articles
- multinomial response (cumulative logit model), and in the Poisson regression (log-linear model). Secondly, Bayesian ... MCMC in the logistic and Poisson regression model with random effects whose distribution is specified...
- Referenced in 31 articles
- mixed-effects (conditional) logistic and Poisson regression models). Finally, the package provides functionality for fitting...
- Referenced in 33 articles
- Boltzmann electrostatics calculations. Continuum solvation models, such as Poisson–Boltzmann and Generalized Born methods, have...
- Referenced in 17 articles
- Bivariate Poisson and diagonal inflated bivariate Poisson regression models in R. In this paper ... bivariate and diagonal inflated bivariate Poisson regression models. An Expectation-Maximization (EM) algorithm is implemented...
- Referenced in 88 articles
- Poisson-Boltzmann equation (PBE), one of the most popular continuum models for describing electrostatic interactions...
- Referenced in 9 articles
- sequencing data based on a Poisson model. Implements the methods described in the paper, Witten ... Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics...
- Referenced in 51 articles
- electrostatic analysis. The Poisson–Boltzmann equation (PBE) is an established model for the electrostatic analysis...
- Referenced in 65 articles
- fixed and random effects enter nonlinearly. These models have a wide variety of applications ... having either a standard form (normal, binomial, Poisson) or a general distribution that you code ... programming statements. PROC NLMIXED fits nonlinear mixed models by maximizing an approximation to the likelihood...
- Referenced in 7 articles
- flexible method for estimating an ICA model. This approach estimates the densities for each component ... functions are estimated via a GAM poisson model. Details can be found in ”Elements...
- Referenced in 61 articles
- thermo-elastic-plastic, and linear viscoelastic material models are implemented. Nearly incompressible behavior that arises ... plasticity problems and elasticity problems with Poisson’s ratio approaching 0.5 is accounted...
- Referenced in 12 articles
- Bartolucci et al. (2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models...
- Referenced in 5 articles
- package Rchoice: Discrete Choice (Binary, Poisson and Ordered) Models with Random Parameters. An implementation ... Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional...
- Referenced in 5 articles
- autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen...
- Referenced in 4 articles
- linear models, binomial and logistic models, Poisson and multinomial models and other generalized regression models...
- Referenced in 2 articles
- Beta-Poisson model for single-cell RNA-seq data analyses. Results: We introduce a beta ... Poisson mixture model that can capture the bimodality of the single-cell gene expression distribution ... well characterized by the beta-Poisson model; the model-fit from BPSC is better than ... standard gamma-Poisson model in > 80% of the transcripts. Moreover, in differential expression analyses...
- Referenced in 6 articles
- spatial regression models for count data. We examine not only the Poisson ... distribution but also the generalized Poisson capable of modeling over-dispersion, the negative Binomial ... well as the zero-inflated Poisson distribution which allows for excess zeros as possible response ... distribution. We add random spatial effects for modeling spatial dependency and develop and implement MCMC...
- Referenced in 50 articles
- Generalized Boosted Regression Models. This package implements extensions to Freund and Schapire’s AdaBoost algorithm ... distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential...