A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Also includes a number of advanced generalized linear model functions including new Tweedie and Digamma glm families and a secure convergence algorithm.

Data analysis, linear models and differential expression for microarray data.

Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized ...

A set of estimators and tests for panel data econometrics.

Maximum Likelihood estimation of random utility discrete choice models (logit and probit).

Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.

Model fitting and evaluation tools for double generalized linear models (DGLMs). This class of models uses one generalized linear model (GLM) to fit the specified response and a second GLM to fit the deviance of the first model.

The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the ...

Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

Likelihood inference based on higher order approximations for linear nonnormal regression models.

Higher order likelihood-based inference for logistic and loglinear models.