The R Book

Summary of Statistical Models in R

Models are fitted using one of the following model-fitting functions:

lm	fits a linear model with normal errors and constant variance; generally this is used for regression analysis using continuous explanatory variables.
aov	fits analysis of variance with normal errors, constant variance and the identity link; generally used for categorical explanatory variables or ANCOVA with a mix of categorical and continuous explanatory variables.
glm	fits generalized linear models to data using categorical or continuous explanatory variables, by specifying one of a family of error structures (e.g. Poisson for count data or binomial for proportion data) and a particular link function.
gam	fits generalized additive models to data with one of a family of error structures (e.g. Poisson for count data or binomial for proportion data) in which the continuous explanatory variables can (optionally) be fitted as arbitrary smoothed functions using non-parametric smoothers rather than specific parametric functions.
lme	and lmer fit linear mixed-effects models with specified mixtures of fixed effects and random effects and allow for the specification of correlation structure amongst the explanatory variables and autocorrelation of the response variable (e.g. time series effects with repeated measures). lmer allows for non-normal errors and non-constant variance with the same error families as a GLM.
nls	fits a non-linear regression model via least squares, ...