Bioc 3.22

What's Changed

• Remove deprecated old framework by @stemangiola in #215
• Refactor error handling in check_and_install_cmdstanr function by @stemangiola in #214
• Sum to zero variable by @stemangiola in #211
• Improve 2d plot by @stemangiola in #216
• Simplify code by @stemangiola in #217
• update paths by @stemangiola in #219
• Add cache_stan_model parameter to relevant functions and update docum… by @stemangiola in #222
• Improve error handling and clarity. Updated parameter handling to use… by @stemangiola in #223
• Remove deprecated .sample argument handling in sccomp_estimate funct… by @stemangiola in #236

Full Changelog: v2.1.11...v2.1.18

v2.1.11

What's Changed

• deprecate remove variation for remove effects by @stemangiola in #205
• Enhance sccomp_predict and summary_to_tibble functions to support rob… by @stemangiola in #207
• Refactor tests for replicate_data to handle X_random_effect_unseen by @stemangiola in #209
• Fdr plot messaging by @stemangiola in #208
• Fix converge metrics for random effect by @stemangiola in #210
• Adapt to ggplot2 s7 by @stemangiola in #213
• Add pkgdown workflow for R package deployment by @stemangiola in #212

Full Changelog: v2.1.9...v2.1.11

v2.1.9 Fix design matrix for edge cases with NA factors

What's Changed

• Enhance NA handling in complex interaction design matrix by @stemangiola in #206

Full Changelog: v1.99.19...v2.1.9

v1.99.19 Allow unknown factors and new groups

What's Changed

• Improve random effects by @stemangiola in #195
• Check columns of tidy eval by @stemangiola in #166
• merge matser by @stemangiola in #196
• Improve args type by @stemangiola in #198
• Reorganise methods by @stemangiola in #200
• Add GitHub Actions workflow for R-CMD-check with CmdStan integration by @stemangiola in #202
• Add prettydoc package to GitHub Actions workflow dependencies by @stemangiola in #203
• Add print method for sccomp objects and related functionality by @stemangiola in #201
• Update prepare_replicate_data to use original_formula_composition… by @stemangiola in #204

Full Changelog: v1.99.18...v1.99.19

API robustness

What's Changed

• add check and test by @stemangiola in #176
• update model by @stemangiola in #168
• add check for factor consistency by @stemangiola in #134
• fix factor testing by @stemangiola in #179
• fix check for ~ 1 formula by @stemangiola in #181
• Improve boxplot by @stemangiola in #183
• Create pkgdown.yaml by @stemangiola in #173
• improve the documentation of sccomp_proportional_fold_change by @stemangiola in #185
• If estimate and remove_outliers are calculated with different fit met… by @stemangiola in #188
• track fit method in the attributes by @stemangiola in #189
• 9 significant figures by @stemangiola in #182

Full Changelog: v1.99.8...v1.99.18

v2.1.7 Fixes edge cases and warnings

What's Changed

• Fix fold prop change calculation by @stemangiola in #170
• change the way I check package by @stemangiola in #171

Full Changelog: v2.1.6...v2.1.7

Adjust versioning for Bioconductor to x.99.z for major update

Full Changelog: v2.1.7...v1.99.8

v2.1.6

This release introduce improvements in visualisation and result reporting and several fixes.

What's Changed

• Fix boxplot with no significance by @stemangiola in #158
• version UP by @stemangiola in #161
• add parameter, tests and docs by @stemangiola in #164
• Fixes for hca by @stemangiola in #162

Full Changelog: v1.9.1...v2.1.6

Major update random effect + proportion input + cmdstanr backend

We are thrilled to introduce a host of significant updates and new features in this latest release of sccomp. These enhancements are designed to provide you with more powerful tools for compositional data analysis, improve usability, and offer greater flexibility in your workflows.

1. Support for Random Effects Modeling

One of the most substantial additions is the implementation of random effects modeling within the sccomp framework. This feature allows you to incorporate hierarchical or nested data structures into your analyses, which is particularly beneficial when dealing with complex experimental designs.

Key Advantages:

• Hierarchical Data Analysis: You can now model data that has multiple levels of variability, such as measurements nested within subjects or samples collected across different time points.
• Flexibility in Model Specification: The inclusion of random effects provides greater flexibility in specifying models that accurately reflect the underlying structure of your data.

2. Direct Input of Proportion Data

We have introduced the ability to input proportion data directly into the sccomp functions. This should not be used if counts are present. It is though to model proportions when counts are not available, for example as result of deconvolution.

Key Advantages:

• Greater Data Compatibility: Allows for the integration of data from different sources that may already be in proportion form.
• Enhanced Flexibility: Facilitates the analysis of data types where counts are not available, such as percentages or fractions.

3. Refactoring and Performance Improvements

Significant effort has been put into refactoring the codebase and optimizing performance. This includes rebasing the master branch and cleaning up the code to enhance readability and maintainability.

Key Enhancements:

• Codebase Streamlining: Multiple rebasing efforts (#45, #54, #125, etc.) have resulted in a cleaner, more efficient codebase.
• Model Function Improvements: Refactoring of model functions (#150, #152) enhances computational efficiency and eases future development.
• Nested Grouping with Cmdstanr: Integration of nested grouping capabilities using cmdstanr (#151, #153) allows for more sophisticated statistical modeling.

4. Enhanced Customization and Control

We have added features that give you more control over the analysis process and outputs.

Key Enhancements:

• Custom Output Samples for Variational Bayes: You can now specify the number of output samples when using variational Bayes methods (#137), allowing you to balance between computational speed and estimation precision.
• Deprecation of Redundant Arguments: Cleaning up the function arguments (#155) makes the functions easier to use and reduces confusion.
• Residual Calculation Updates: Changes to how residuals are calculated (#124) improve the accuracy of model diagnostics.

5. Documentation and Usability Improvements

We recognize the importance of clear documentation and have made substantial updates to enhance your user experience.

Key Enhancements:

• Updated README and Vignettes: The README file and accompanying vignettes have been thoroughly updated (#141) to reflect all new features and provide detailed guidance on how to use them.
• Attribute Passing Improvements: Modifications to how attributes are passed between functions (#140) improve the consistency and reliability of the package.
• User Messages and Warnings: Informative messages have been added (#148) to help you understand the progress of computations and alert you to potential issues.

6. Additional Features and Fixes

Several other enhancements and bug fixes have been implemented to improve the overall functionality of sccomp.

Key Enhancements:

• Proportion Difference Calculation: A new feature to calculate the difference in proportions directly (#147), aiding in the interpretation of results.
• Environment Handling in Formulas: Adjustments to formula handling (#142) prevent potential errors related to variable scope and environment.
• Instantiation and Initialization Improvements: Enhancements to how models are instantiated (#136) lead to more stable and faster model fitting.

Full Changelog: https://github.com/MangiolaLaboratory/sccomp/compare/v1.7.12…v1.9.1

For a comprehensive overview of all changes and detailed instructions on how to utilize the new features, please refer to the README.

We believe these updates will significantly enhance your data analysis capabilities using sccomp. The support for random effects modeling and direct proportion data input, in particular, open up new avenues for sophisticated and flexible analyses. We are committed to continuous improvement and welcome any feedback you may have.

Thank you for your continued support, and we hope you find these new features valuable in your research.

What's Changed PR list

• rebase master by @stemangiola in #45
• rebase by @stemangiola in #54
• rebase by @stemangiola in #125
• rebase master by @stemangiola in #128
• rebase by @stemangiola in #131
• rebase by @stemangiola in #132
• Instantiate by @stemangiola in #136
• allow custom output samples for vb by @stemangiola in #137
• update functions to be exposed by @stemangiola in #139
• pass the attribute by @stemangiola in #140
• update README and vignette by @stemangiola in #141
• rebase by @stemangiola in #143
• drop environment from formula and quotes by @stemangiola in #142
• rebase by @stemangiola in #144
• Calculate proportion difference by @stemangiola in #147
• add message by @stemangiola in #148
• Refactor model functions by @stemangiola in #150
• Cmdstanr nested grouping by @stemangiola in #151
• Refactor model functions by @stemangiola in #152
• Cmdstanr nested grouping by @stemangiola in #153
• Cmdstanr by @stemangiola in #84
• deprecate argument by @stemangiola in #155
• Change residuals by @stemangiola in #124
• Proportions by @stemangiola in #126

Full Changelog: v1.7.12...v1.9.1

More permissive logit threshold for significance

What's Changed

• fif checks by @stemangiola in #129

Full Changelog: v1.7.7...v1.7.12