CRAN release 0.1.1

New features

• Firth bias-reduced GLMs (firth = TRUE) now work for all standard
  families (gaussian, binomial, Poisson, Gamma, inverse.gaussian) on
  dense, sparse, and streaming backends. In 0.1.0 Firth was limited to
  binomial(link = "logit") on dense designs only.

• SQUAREM acceleration (Varadhan and Roland, 2008) for the
  fastglm_zi() EM driver, converting linear EM convergence to
  near-quadratic.

Numerical stability

• Negative-binomial initialization in fastglm_nb() now uses a
  moment-based mu/theta seed instead of always running a full pilot
  Poisson fit, improving convergence on overdispersed data and near
  boundary cases.

• Increased the default outer.maxit for fastglm_nb() from 25 to
  50 to allow convergence in harder NB problems.

• Clamping guards in native C++ family kernels for inverse-link and
  sqrt-link families (Gamma, inverse.gaussian, Tweedie) to prevent
  overflow when eta is near zero.

• Floor on mu.eta for Tweedie inverse and sqrt links to avoid
  division by zero in IRLS weights.

• Sign correction in the Tweedie sqrt mu.eta kernel.

Bug fixes

• Replaced Rf_error() with Rcpp::stop() in bigmemory.cpp
  (reported in GitHub issue).

Documentation

• New vignette firth-fastglm covering Firth bias-reduced GLMs for
  all supported families.

• New vignettes nb-convergence-fastglm and nb-stability-fastglm
  demonstrating fastglm_nb() convergence and stability relative to
  MASS::glm.nb() on challenging datasets.

• Expanded benchmarks vignette with additional model classes.

Internal

• Shared Brent root-finder moved to inst/include/brent.h,
  replacing three duplicated implementations.

• Expanded test suite (107 test blocks / 361 expectations, up from
  54 / 131 in 0.1.0), including new suites for numerical edge cases
  and Firth across all families.