femtograd: Automatic Differentiation for Statistical Computing

Automatic differentiation via reverse-mode AD (backpropagation) for first-order gradients and forward-over-reverse AD for Hessian computation. Includes log-likelihood functions for exponential family distributions, MLE optimization with base R generics, hypothesis testing, profile likelihood, bootstrap inference, and model diagnostics. Designed for pedagogy and modern statistics rather than large-scale ML.