### autoFC 0.1.2  (Published Jun 7th, 2021)

* The initial version of `autoFC` accepted by *Applied Psychological Measurement* (Li et al., 2022).

* Designed for reducing the cognitive load of manually constructing forced-choice blocks based on multiple criteria 
(e.g., social desirability matching, item loading, trait pairing)
Includes the core functionality for automatically pair statements in each forced-choice block.


### autoFC 0.2.0.1001  (Published Feb 17th, 2024)

* The version used for the paper accepted by *Organizational Research Methods* (Li et al., 2025).

* Improved functionality allowing users to construct pre-defined forced-choice blueprints and full FC based on these blueprints

* Wrapper functions for more user-friendly response conversion, data pre-processing and scoring using the `thurstonianIRT` package.

* Various diagnostic functions added.

### autoFC 0.2.0.1002  (Published Mar 13th, 2025)

* Minor bug fixes and planned for future updates

### autoFC 0.2.0.1010  (Published Apr 29th, 2026)

* Remove dependencies on the `irrCAC` package to resolve the package archiving issue.

### autoFC 1.0.0.1000  (Published May 27th, 2026)

This major update introduces critical performance, usability, and mathematical enhancements. 
These features are added to allow for MUCH easier and convenient use of FC scales, than ever before.

* Thurstonian Factor Model (TFM) and Thurstonian IRT (IRT) Support: 
Native syntax generation and scoring for highly stable first-order and second-order models in lavaan and Mplus, 
completely bypassing tedious manual constraint declarations. No dependencies on `thurstonianIRT` package is needed now.

* Vectorized, C-Level Algorithmic Speedups: Restructured simulated annealing energy calculators 
and agreement metrics (bp.coeff.raw, gwet.ac1.raw) to achieve significant performance improvements.

* High-Performance Stan Integration: A unified Stan pipeline featuring within-chain parallel computing 
(reduce_sum) and automatic logical dependency reduction (Heister, Doebler, & Frick, 2025) to easily scale to larger blocks.

* Blazing Fast Scoring: Fast, vectorized MAP scoring in R using gradients to score participants 
and calculate Standard Errors in seconds (R and Mplus models).

* Enhanced Blueprint Functions and Support for Global Optimization: Users have always been wondering whether we can optimize
FC measures (on block level) while strictly following a certain designation of trait pair and/or keying distribution or strictly aligning with 
a pre-defined blueprint (on global scale level). We have now implemented easy-to-use functionality serving exactly for this purpose. 


### autoFC 1.0.0.1001  (Current Version)

* Bug fixes in `generate_tirt_mplus_syntax()` function where originally the designation of `trait_col` and `key_col` parameters have no effect.



Li, M., Sun, T., & Zhang, B. (2022). AutoFC: An R package for automatic item pairing in forced-choice test construction. 
*Applied Psychological Measurement, 46*(1), 70-72. https://doi.org/10.1177/01466216211051726

Li, M., Zhang, B., Li, L., Sun, T., & Brown, A. (2025). Mixed-keying or desirability-matching in the construction of forced-choice measures? An empirical investigation and practical recommendations. 
*Organizational Research Methods, 28*(2), 296-329. https://doi.org/10.1177/10944281241229784

