Changes in version 1.2.6 (2026-09-23)                  

Descriptive and Goodness-of-Fit Capabilities

  - Unified Degree Distributions: The separate degree_distribution and
    spilloverdegree_distribution methods of class iglm.data have been
    merged into a unified, flexible degree_distribution(x_i, x_j, y_i,
    y_j, mode) method. Users can compute degree distributions on induced
    subnetworks where $x_i, x_j, y_i,$ and $y_j$ are constrained to
    specific values.
      - Attributes can be filtered using exact values (e.g., 0 or 1),
        discrete sets, continuous above-mean indicators (where $x_i >
        \bar{x}$ for continuous/count covariates), or custom filtering
        functions.
      - Users can isolate directional spillover pathways: outgoing ties
        quantify spillover sending capacity, while incoming ties
        quantify spillover exposure.
  - Extended Network Descriptive Distributions: The
    edgewise_shared_partner_distribution,
    dyadwise_shared_partner_distribution, and
    geodesic_distance_distribution methods of iglm.data now also accept
    x_i, x_j, y_i, y_j, and mode arguments to evaluate structural
    properties on attribute-conditioned subgraphs.
  - Enhanced Goodness-of-Fit (assess): Because the assess method
    evaluates descriptive statistics specified on the left-hand side of
    formula terms, the entire goodness-of-fit toolbox seamlessly extends
    to these generalized, attribute-conditioned distributions.
  - Global vs. Local Modes: Added the mode parameter ("global" by
    default, or "local") to all descriptive distributions, allowing
    users to restrict evaluations either to all network ties or solely
    to ties between units with overlapping neighborhoods ($\mathcal{N}_i
    \cap \mathcal{N}_j \neq \emptyset$).

Consistency and Alignment of Terminology

  - family Parameter: Renamed the type parameter (type_x, type_y) to
    family (family_x, family_y) to align with standard R conventions
    (such as glm()) for specifying the conditional distributions of
    attributes.
  - Unified Unit Terminology: Standardized terminology throughout the
    package code, function arguments (e.g., n_units replacing n_actor),
    and documentation to consistently refer to network entities as
    "units" rather than mixing "actors" and "units".
  - Shorthand Distribution Aliases: Added convenient shorthand methods
    to the iglm.data class:
      - deg_dist() for degree_distribution()
      - geo_dist() for geodesic_distance_distribution()
      - esp_dist() for edgewise_shared_partner_distribution()
      - dsp_dist() for dyadwise_shared_partner_distribution()

Labeling of Attributes and Terms

  - Custom Attribute and Network Labels: Users can now supply custom
    labels via label_x, label_y, and label_z when constructing iglm.data
    objects.
  - Enhanced Output Formatting: summary() and print() methods for both
    iglm fitted models and iglm.data objects display informative,
    domain-specific labels instead of generic canonical identifiers
    (e.g., attribute_republican and spillover_republican_turnout), with
    options to toggle between labeled and canonical names.

Robustness and Input Validation

  - Formula Scope Verification: Added environment checks ensuring that
    all objects referenced in model formulas exist in the calling
    environment before estimation begins.
  - Numerical Safeguards: Strengthened optimization and estimation
    routines to prevent silent propagation of NA or NaN values.
  - Model Term Validation (check.IglmTerm): Introduced comprehensive
    validation helpers for model terms, checking mandatory arguments,
    expected data types (scalars, matrices), allowed categorical values,
    and positional-to-named argument mappings.
  - Descriptive Formula Traps: Added informative error messages when
    formulas are incorrectly specified (e.g., passing raw vectors or
    lists instead of an iglm.data object on the LHS).