Changes in version 0.1.0.9000                      

  - The vignette plots now restore the graphical parameters they change,
    and the vignettes no longer set the data.table thread count.

                 Changes in version 0.1.0 (2026-11-03)                  

First release.

Module scores

  - calculate_WSS() computes Weighted Sum Score module-level summaries
    from proteomic data, using STRING network connectivity and
    biomarker-association weighting to align and aggregate protein-level
    signal into module scores. Supports dedup_by and min_module_size
    options for controlling how genes assigned to multiple modules are
    deduplicated and how small modules are filtered.
  - load_prebuilt_wss(), list_prebuilt_wss(), and load_prebuilt_terms()
    provide access to a bundled reference module/weighting table
    ("core_AD_plasma_biomarkers") and its module term labels.
  - calculate_WSS() accepts a data frame as result (converted to a
    data.table) and rejects other classes with an informative error.
  - calculate_WSS() stops with a clear error that names the affected
    proteins when a protein used by a module has a missing value. This
    also covers the case where every module has one, which previously
    returned NA scores without any message.
  - The documentation now states that a module with none of its proteins
    in the matrix is reported with all-NA scores and a module_sd of 0.

Prediction from core biomarkers

  - predict_WSS() predicts all 75 module scores for the
    "core_AD_plasma_biomarkers" reference set from Age, Gender, and 8
    core plasma biomarkers, using a bundled joinet two-layer elastic net
    model, without requiring the full proteomic panel.
  - normalize_wss_biomarkers() builds the rank-normalized biomarker
    inputs predict_WSS() requires from raw biomarker values, via either
    method = "project" (projecting onto a bundled, anonymized baseline
    reference distribution, using project_rank_norm() -- supports a
    single new patient) or method = "self" (rank-normalizing within your
    own cohort).
  - project_rank_norm() and load_prebuilt_biomarker_reference() expose
    the underlying projection utility and reference distribution
    directly.

Documentation

  - Added a citation (citation("WSSmod")) and links to the accompanying
    preprint.
  - New vignette, "Calculating Weighted Sum Scores", covering input
    normalization, the bundled and custom module tables, and scoring new
    samples against a reference cohort.
  - New vignette, "Predicting Weighted Sum Scores from plasma
    biomarkers", covering the biomarker inputs, the two normalization
    methods, and reading the predicted module scores.
  - New vignette, "Exploring the bundled datasets", describing each
    bundled file, its loader and what it can be used for.
  - A package-level help page (?WSSmod) gives an overview of the
    functions and bundled data.
  - calculate_WSS() now documents what input scale it expects.

Example data

  - wss_example_expression and wss_example_biomarkers are small, fully
    simulated datasets (no participant data) for trying out
    calculate_WSS(), normalize_wss_biomarkers() and predict_WSS().

The bundled model and reference data were verified end-to-end against
the original analysis's held-out follow-up cohort, reproducing published
predictions to floating-point precision.