Description The package contains functions that 1) estimates gene-environment interaction between a SNP and a continuous non-genetic attribute by fitting a generalized additive model to case-parent trio data, 2) produces graphical displays of estimated interaction, 3) performs permutation test of gene-environment interaction; 4) simulates informative case-parent trios.

In many data sets from clinical studies there are patients insusceptible to the occurrence of the event of interest. Survival models which ignore this fact are generally inadequate. The main goal of this paper is to describe an application of the generalized additive models for location, scale, and shape (GAMLSS) framework to the fitting of long-term survival models. In this work the number of ...

The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from th...

In many cases statisticians are not only required to provide optimal fits or classification results but also to interpret and visualize the fitted curves or discriminant rules. A main issue here is to explain in what way the explanatory variables impact the resulting fit. The R package KernGPLM (currently under development) implements semiparametric extensions to the generalized linear regressi...

OBJECTIVE To analyze the association between concentrations of air pollutants and admissions for respiratory causes in children. METHODS Ecological time series study. Daily figures for hospital admissions of children aged < 6, and daily concentrations of air pollutants (PM10, SO2, NO2, O3 and CO) were analyzed in the Região da Grande Vitória, ES, Southeastern Brazil, from January 2005 to Decemb...

Generalized additive model is a powerful statistical learning and predictive modeling tool that has been applied in wide range of applications. The need high-dimensional eminent the context dealing with high through-put data such as genetic analysis. In this article, we studied two step selection estimation method for ultra dimensional generalized models. first applies group lasso on expanded b...

The functional generalized additive model (FGAM) was recently proposed in McLean et al. (2012) as a more flexible alternative to the common functional linear model (FLM) for regressing a scalar on functional covariates. In this paper, we develop a Bayesian version of FGAM for the case of Gaussian errors with identity link function. Our approach allows the functional covariates to be sparsely ob...