CRAN release 0.1.0

fastglm 0.1.0

New features

• New top-level function fastglm_nb() for negative-binomial
  regression with the dispersion theta estimated jointly with the
  regression coefficients. Plays the same role as MASS::glm.nb(),
  but the IRLS loop, the inner Brent root-find for theta, and the
  outer (beta, theta) alternation all run entirely in C++.

• New top-level function fastglm_hurdle() for two-part count models
  with a binary zero / non-zero component and a zero-truncated Poisson
  or NB count component. Same model as pscl::hurdle(); the joint fit
  runs in a single C++ driver that calls the existing IRLS solver
  twice and runs an inner Brent MLE for theta in the unknown-theta
  NB case.

• New top-level function fastglm_zi() for zero-inflated Poisson and
  NB regression. Same model as pscl::zeroinfl(); the entire EM
  driver runs in C++, including closed-form posterior responsibilities,
  weighted IRLS for both M-steps, the inner Brent MLE for theta, and
  the analytical observed-information vcov.

• New firth = TRUE argument to fastglm() and fastglm_fit() for
  Firth's bias-reducing penalty on the score. Currently supported for
  family = binomial(link = "logit") on dense designs; converges in
  finite steps under separation, where unpenalized IRLS would diverge.
  Coefficients agree with logistf::logistf() to 1e-7 on the standard
  Heinze-Schemper test cases.

• New built-in negbin(theta, link) family for negative-binomial
  regression with known dispersion, dispatched to a native C++ kernel
  on the log, sqrt, and identity links. Drop-in for
  MASS::negative.binomial(theta, link) without a hard dependency on
  MASS.

• Native fast paths for the family$family == "Negative Binomial(K)"
  form produced by MASS::negative.binomial(), the
  quasibinomial() / quasipoisson() families, and statmod::tweedie().
  Detection is automatic.

Documentation

• New vignette count-firth-fastglm covering fastglm_nb(),
  fastglm_hurdle(), fastglm_zi(), and the firth = TRUE flag, with
  small inline accuracy and timing comparisons against MASS::glm.nb,
  pscl::hurdle, pscl::zeroinfl, and logistf::logistf.

• New vignette benchmarks-fastglm providing a more comprehensive
  benchmark study at larger sample sizes, covering all six model
  classes. Pre-compiled at maintainer build time so it does not run
  during R CMD check / R CMD build and adds essentially zero time
  to the package check budget.

• fastglm-overview updated with a short section introducing the new
  count-data and Firth entry points.

Other new features

• Real vcov() for "fastglm" objects: the unscaled covariance is now
  computed in C++ from the IRLS factorisation directly and exposed as
  $cov.unscaled. summary.fastglm(), vcov.fastglm(), and
  predict.fastglm(se.fit = TRUE) now all work without a refit hack;
  vcov.fastglmFit() no longer re-runs glm.fit to recover a qr slot.

• S3 methods registered on sandwich::vcovHC() and sandwich::vcovCL()
  for heteroskedasticity-consistent (HC0–HC3) and cluster-robust covariance
  matrices. After library(sandwich), vcovHC(fit) and vcovCL(fit, ...)
  match sandwich::vcovHC.glm() / sandwich::vcovCL.glm() to
  floating-point precision and work for sparse, big.matrix, and
  in-memory fits. (Earlier development versions shipped local
  vcovHC() / vcovCL() generics; those have been removed in favour of
  registering on the canonical sandwich generics.)

• Sparse design matrices (Matrix::dgCMatrix) are now supported directly
  by fastglm() and fastglm_fit() for the LLT (method = 2) and LDLT
  (method = 3) Cholesky paths. Other decompositions are rejected with
  an informative error rather than silently densified.

• bigmemory::big.matrix inputs now stream the design matrix in
  row-blocks of FASTGLM_CHUNK_ROWS rows (default 16384,
  user-configurable via the environment variable). Filebacked
  big.matrix objects no longer have to be materialised in RAM.

• New top-level function fastglm_streaming(chunk_callback, n_chunks, family, ...) for fitting GLMs on data sources that do not fit in
  memory: Arrow datasets, Parquet files, DuckDB queries, CSV streamers,
  and any other chunk-yielding iterator. The IRLS loop, step-halving,
  and Cholesky solve all run in C++; the closure is invoked only to
  deliver one row-block at a time.

Speed

• For standard families (gaussian / binomial / poisson / Gamma /
  inverse.gaussian on their common links) the per-iteration calls to
  family$variance, mu.eta, linkinv, and dev.resids are now
  evaluated in inline C++ rather than via R callbacks. Detection is
  automatic; non-standard families fall back to the previous R-callback
  path with no user-facing change.

• The IRLS solver pre-allocates its working buffers across iterations
  and uses noalias() writes throughout solve_wls(). Eigen's
  parallelism is no longer disabled.

• On large n the combined effect is roughly a 1.5×–2× speed-up over
  fastglm 0.0.4 on the same hardware, on top of the existing 3×–10×
  advantage over stats::glm().

Documentation

• New vignette fastglm-overview providing a single high-level entry
  point covering all of the package's functionality.

• New vignette large-data-fastglm walking through the three
  large-data paths (sparse, big.matrix, streaming callback) end to
  end, including the Arrow / Parquet recipe.

Internal

• New tests/testthat/ suite covering vcov(), native vs callback
  family dispatch, sparse fits, big.matrix streaming, callback-based
  streaming, and robust SE.

CRAN release 0.0.3

• Moves c++ header files to inst/install so source c++ code can be used externally

CRAN release 0.0.1

First release of the fastglm package to CRAN (version 0.01)