puisR

puisR is a software package developed for the R programming language, designed to facilitate the analysis of spatially and temporally varying count data. It aims to provide a flexible framework for fitting various statistical models to such data, particularly those exhibiting Poisson or related distributions. The package offers functionalities for data manipulation, model specification, parameter estimation, and diagnostic checking.

One of the primary strengths of puisR lies in its ability to handle complex spatial and temporal dependencies. It allows users to incorporate covariates and define correlation structures that capture the heterogeneity and interdependence inherent in many real-world count datasets. This can include applications in fields such as ecology, epidemiology, and environmental science where events are often clustered in space and time.

The package supports a range of modeling approaches, including generalized linear models with spatial or temporal correlation. Users can specify different types of correlation structures, such as autoregressive processes or Gaussian processes, to better represent the underlying data generating mechanisms. The estimation of model parameters is typically performed using maximum likelihood methods.

puisR also provides tools for visualizing model outputs and assessing the goodness-of-fit. This includes functions for plotting predicted values, residuals, and spatial or temporal patterns. By offering a comprehensive set of tools, puisR aims to simplify the process of analyzing complex count data, enabling researchers to gain deeper insights into the factors driving observed patterns.