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Changelog

Source: NEWS.md

plmmr 4.0.0 (2024-10-07)

  • Major re-structuring of preprocessing pipeline: Data from external files must now be processed with process_plink() or process_delim(). All data (including in-memory data) must be prepared for analysis via create_design(). This change ensures that data are funneled into a uniform format for analysis.

  • Re-worked vignettes: The vignettes/articles for the package are now all revised to include examples of the complete pipeline with the new create_design() syntax. There is an article for each type of data input (matrix/data.frame, delimited file, and PLINK).

  • CRAN submission: We updated several items in the documentation in order to prepare for CRAN submission.

plmmr 3.2.0 (2024-09-02)

  • bigsnpr now in Suggests, not Imports: The essential filebacking support is now all done with bigmemory and bigalgebra. The bigsnpr package is used only for processing PLINK files.

  • dev branch gwas_scale has a version of the pipeline that runs completely file-backed.

plmmr 3.1.0 (2024-07-13)

  • Enhancement: To make plmmr have better functionality for writing scripts, the functions process_plink(), plmmm(), and cv_plmm() now (optionally) write ‘.log’ files, as in PLINK.

  • Enhancement: In cases where users are working with large datasets, it may not be practical or desirable for all the results returned by plmmm() or cv_plmm() to be saved in a single ‘.rds’ file. There is now an option in both of these model fitting functions called ‘compact_save’, which gives users the option to save the output in multiple, smaller ‘.rds’ files.

  • Argument removed: Argument std_needed is no longer available in plmm() and cv_plmm() functions.

plmmr 3.0.0 (2024-06-27)

  • Bug fix: Cross-validation implementation issues fixed. Previously, the full set of eigenvalues were used inside CV folds, which is not ideal as it involves information from outside the fold. Now, the entire modeling process is cross-validated: the standardization, the eigendecomposition of the relatedness matrix, the model fitting, and the backtransformation onto the original scale for prediction.

  • Computational speedup: The standardization and rotation of filebacked data are now much faster; bigalgebra and bigmemory are now used for these computations.

  • Internal: On the standardized scale, the intercept of the PLMM is the mean of the outcome. This derivation considerably simplifies the handling of the intercept internally during model fitting.

plmmr 2.2.1 (2024-03-16)

  • Name change: Changed package name to plmmr; note that plmm(), cv_plmm(), and other functions starting with plmm_ have not changed names.