Changes in version 0.0.1 (2026-06-22)                  

Initial CRAN release.

  - Formula / data interface to glmnet covering the full glmnet family
    surface: the six character strings (gaussian, binomial, poisson,
    cox, multinomial, mgaussian) plus arbitrary GLM family objects such
    as stats::Gamma(link = "log") and MASS::negative.binomial(theta =
    ...).

  - Predict-time design-matrix reconstruction from the training-time
    terms, xlevels, and contrasts stored on the fit object, independent
    of session-level options("contrasts"). Absent factor-level columns
    are zero-padded; both main effects and interaction terms are aligned
    in one pass.

  - Rank-deficient designs are handled in the spirit of stats::glm(): a
    column-pivoted QR check on the column-centred design matrix
    identifies linearly dependent columns, the underlying glmnet fit
    only sees the independent subset, and the dropped columns surface as
    NA in coef() and summary() so they can be told apart from
    coefficients the L1 penalty shrunk to zero.

  - predict() follows stats::predict.glm() on novel factor levels in
    newdata by default (an error message matching glm's "factor g has
    new levels ..." string). A new predict(fit, newdata, on_new_levels =
    "na") opt-in is provided for batch / production scoring pipelines:
    rows with unseen levels are returned as NA and a warning naming the
    affected row count is emitted, while the rest score normally.

  - Complete-case filtering happens through model.frame(..., na.action =
    na.omit); the dropped and used counts are exposed as fit$nobs_info
    and announced via a one-line message at fit time.

  - A single lambda argument covers the three common selection rules —
    "cv_min", "cv_1se", and "fix" (paired with lambda_value). The chosen
    numeric lambda is stored on fit$lambda_value and reused by
    predict(), coef(), summary(), and plot() so there is a single source
    of truth.

  - S3 methods (print, summary, predict, coef, nobs, plot) mirror the
    glm() surface. summary() follows stats::summary.glm()'s coefficient
    table layout, including a glm-style header "(N not defined because
    of singularities: ...)" when the design was rank-deficient, and a
    permanent footer explaining why no standard errors, z-values, or
    p-values are reported under the current infer = "none" mode
    (shrinkage bias, data-driven lambda selection, and active-set
    conditioning).

  - Accessors as_glmnet() and as_cv_glmnet() expose the underlying
    glmnet and cv.glmnet objects for downstream tooling that consumes
    them directly.

  - Two vignettes: vignette("fbrglm") walks the formula/data interface,
    nobs_info, factor narrowing, and offsets;
    vignette("fbrglm-families") walks the family-by-family worked
    examples plus the piecewise-exponential survival formulation.