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br_seed(), br_seed_screen(),
br_seed_select(), and br_seed_model()
implementing the SEED (Selection of Essential prognostic genes from
Expression Data) pipeline for identifying cancer prognosis target genes
without control samples (#57). Based on Yang et al. (2025) Cancer
Letters, DOI: 10.1016/j.canlet.2025.217960. The four-step pipeline
includes:
br_seed_screen): Univariate screening via
Cox/logistic regression or Spearman correlation against one or more
clinical indicators, with p-value threshold filtering and significance
intersection across indicators.enrich
argument).br_seed_select): Sequential gene selection
through Lasso regularization (glmnet), multivariate regression, and
stepwise selection with reproducibility via seed.br_seed_model): Risk score model construction
with coefficient extraction and optional z-score expression scaling. All
functions return S3 list objects with cli-based print methods, reuse the
existing br_pipeline() engine for batch regression, and
support mirai parallelization via n_workers.br_get_model_stats() returning a tidy data.frame
of per-model summary statistics (N, events, C-index, AIC, LR test p, PH
test p for Cox; N, AIC, deviance for GLM; N, R-squared for LM),
eliminating the need for manual supplementary model fitting to obtain
diagnostic metrics (#68).br_diagnose().FGFR3::TACC3, EGFR-AS1,
1p/2q, gene name, R reserved
words (if, TRUE, NA), and
backtick-quoted user input in x, x2, and
y variables (#69). Refactored repair_names()
with remove_backticks() for robust quoting, and applied
quoting to response variables in br_set_y().biocViews field to DESCRIPTION for Bioconductor
Suggests compatibility.br_compare_models() and
br_show_forest_comparison() (#54).qs with
qs2.br_show_fitted_line() and
br_show_fitted_line_2d() documentation for compatibility
with visreg 3.0, which removes the gg
argument and renames line.par, fill.par,
points.par to line, fill,
points (#65).devtools::check(env_vars = c('_R_CHECK_DEPENDS_ONLY_' = "true"))
and fixed reported check issues.br_pipeline() to reduce running
time.br_show_coxph_diagnostics().Enhancements & New Features:
br_show_forest_circle()for circular forest
plots.br_show_coxph_diagnostics().br_show_nomogram()for clinical prediction
modeling.dry_runoption to br_pipeline()for
pipeline validation.Fixes & Improvements:
br_show_nomogram().br_show_residuals() to show residuals vs fitted
plot for regression models.br_set_x() and br_set_x2() to
properly handle different input types, including valid column names (in
R), invalid column names, and model formula terms.br_get_model_names() and
br_rename_models() to get and set model names.run_parallel in
br_run().br_get_model() by merging it with
br_get_models().options().exponentiate option appropriately in
br_run().br_avail_methods_use_exp() to include
c("poisson", "quasipoisson").log_first option to
br_show_forest().: for interaction term
combinations throughout the package.br_show_forest().br_get_model() couldn’t
properly process multiple string inputs for the idx
parameter.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.