exametrika 1.8.1

Bug Fixes

dataFormat Function

• Fixed factor ID column detection: dataFormat() now correctly identifies factor-type ID columns before converting factors to numeric. Previously, the factor-to-numeric conversion occurred before ID detection, causing factor ID columns with many levels (>=20) to trigger a “Too many categories” error instead of being recognized as IDs.
• Removed unused helper function: Removed dead code (is_response_data()) that was defined but never called.

GridSearch Function

• Fixed ordinal data support: GridSearch now correctly handles ordinal data by using obj$Q instead of obj$U for test length calculation
• Resolved nfld=1 parameter issue: Eliminated invalid parameter ranges that caused crashes with ordinal datasets

Biclustering.ordinal Function

• Enhanced numerical stability: Added pmax(Ufcq_prior, 1e-10) to prevent division by zero and NaN errors
• Fixed convergence failures: Resolved “missing value where TRUE/FALSE needed” errors in specific parameter combinations (e.g., ncls=4 with nfld=5)
• Improved robustness: Algorithm now handles edge cases where field membership probabilities approach zero

Impact

• GridSearch now works reliably with ordinal data across all parameter combinations
• Biclustering analysis completes successfully for previously problematic parameter settings
• Enhanced overall stability for large-scale grid searches

exametrika 1.8.0

Naming Convention Improvements

This release improves naming consistency across the package while maintaining full backward compatibility through a deprecation path.

New Field Names (Recommended)

All analysis functions now return results with snake_case field names for better consistency:

• n_class - Number of latent classes (replaces Nclass)
• n_field - Number of latent fields (replaces Nfield)
• n_rank - Number of latent ranks (replaces Nrank)
• n_cycle - Number of EM iterations (replaces N_Cycle)
• log_lik - Log-likelihood value (replaces LogLik)

Deprecated Field Names (Still Supported)

The old field names continue to work for backward compatibility:

• Nclass, Nfield, Nrank, N_Cycle, LogLik - Deprecated but functional
• These fields will be removed in version 2.0.0
• Please update your code to use the new snake_case names

Example Migration

# Old code (deprecated but still works)
result <- LCA(data, ncls = 3)
n_classes <- result$Nclass

# New code (recommended)
result <- LCA(data, ncls = 3)
n_classes <- result$n_class

Output Formatting Improvements

Progress messages now display properly in R Markdown documents:

• GridSearch() - Added verbose parameter (default: TRUE)
  • Output organized by row: ncls = 2: nfld=2 nfld=3 nfld=4 ...
  • Set verbose = FALSE to suppress all progress messages
• Biclustering_IRM() - Improved iteration display
  • Format: iter 1: match=0 nfld=15 ncls=30
  • Class adjustment messages: Adjusting classes: BIC=-99592.5 ncls=21 (min size < 20)
• LRA.ordinal() - Fixed verbose behavior
  • Changed default from verbose = TRUE to verbose = FALSE (consistent with binary/rated versions)
  • Saturation Model: Saturation Model - iter 1: log_lik=-1.234567
  • Restricted Model: Restricted Model - iter 1: log_lik=-0.987654
• LRA.rated() - Fixed verbose behavior
  • Changed default from verbose = TRUE to verbose = FALSE (consistent with binary/ordinal versions)
  • Same improved format as LRA.ordinal

All progress messages now use proper line breaks instead of carriage returns, ensuring clean output in R Markdown/knitr documents and web documentation.

Bug Fixes

• Fixed Array plot color mapping for binary data
  • Used sort(unique(...)) to ensure consistent ordering: 0 (white/incorrect) → 1 (black/correct)
  • Previously, color mapping could be reversed depending on data order
• Fixed Ranklustering Array plot sorting order
  • Students with higher correct response rates now appear at the bottom of the plot
  • Previously, high performers were incorrectly placed at the top

Internal Improvements

• Standardized internal variable naming (e.g., testEll → test_log_lik)
• Improved code readability and maintainability
• All output messages now use consistent log_lik terminology

exametrika 1.7.0

• Renamed IRM() to Biclustering_IRM() for consistency with structure learning naming conventions
  • Follows the same pattern as BNM_GA(), LDLRA_PBIL(), etc. (model_method naming)
  • IRM() function still works but is now deprecated with a warning using .Deprecated()
  • The new name clarifies that this function performs Biclustering structure learning using the Infinite Relational Model
  • All documentation and examples updated to use Biclustering_IRM()
• Added .Deprecated() warnings to renamed functions from version 1.6.5
  • StrLearningGA_BNM() now shows deprecation warning, recommending BNM_GA()
  • StrLearningPBIL_BNM() now shows deprecation warning, recommending BNM_PBIL()
  • StrLearningPBIL_LDLRA() now shows deprecation warning, recommending LDLRA_PBIL()
  • Old function names still work for backward compatibility but display warnings

exametrika 1.6.5

• Critical bugfix for LCA() response type validation
  • Fixed incorrect variable reference in response type checking
• Added beta1 and beta2 parameters to all Beta distribution-based functions
  • LRA.binary(): beta1=1, beta2=1 (GTM method)
  • LCA(): beta1=1, beta2=1
  • Biclustering.binary(): beta1=1, beta2=1
  • BNM(): beta1=1, beta2=1
  • LDB(): beta1=1, beta2=1
  • LDLRA(): beta1=2, beta2=2
  • LD_param_est(): beta1=2, beta2=2 (internal helper function)
  • LDLRA_PBIL(): beta1=2, beta2=2
  • These parameters control the prior density parameters in Bayesian parameter estimation
  • Users can now customize Beta distribution parameters for EM algorithm parameter updating
  • Default values preserve backward compatibility with previous versions
• Added alpha parameter to polytomous models for Dirichlet prior control
  • Biclustering.ordinal(): alpha=1 (flat Dirichlet prior)
  • Biclustering.nominal(): alpha=1 (flat Dirichlet prior)
  • These parameters control the concentration parameter for Dirichlet priors in category probability estimation
  • Users can customize Dirichlet parameters to adjust prior strength (alpha > 0)
  • Default values (alpha=1) preserve backward compatibility with previous versions
• Simplified function names for structure learning functions
  • StrLearningGA_BNM() renamed to BNM_GA()
  • StrLearningPBIL_BNM() renamed to BNM_PBIL()
  • StrLearningPBIL_LDLRA() renamed to LDLRA_PBIL()
  • Shorter, more intuitive function names for improved usability
  • All documentation and examples updated accordingly

exametrika 1.6.4

• Critical bugfix for Biclustering() field initialization
  • Fixed subscript out of bounds error when nfld values cause ceiling() to exceed nfld
  • Added pmin() constraint to ensure fld0 values never exceed nfld parameter
  • Resolves crashes with specific nfld/testlength combinations (e.g., nfld=15, testlength=21)
• Enhanced GridSearch() fit index optimization logic
  • Added support for all fit indices returned by TestFitIndices()
  • Correctly handles minimization indices: model_log_like, model_Chi_sq, RMSEA, AIC, CAIC, BIC
  • Correctly handles maximization indices: NFI, RFI, IFI, TLI, CFI
  • Added validation to prevent unknown index specification

exametrika 1.6.3

• Major performance enhancement for GRM (Graded Response Model)
  • Replaced R implementation with high-performance C++ code using Rcpp
  • Implemented analytical gradient computation for significant speed improvements
  • Achieved 5-6x faster convergence compared to numerical differentiation
  • Maintains identical mathematical accuracy to previous implementation
  • Full compatibility with existing GRM() function interface
• Added converge variable to all EM-based functions to indicate algorithm convergence status
  • Functions affected: Biclustering(), LCA(), LRA(), and related methods
  • Returns TRUE if converged within maxiter iterations, FALSE otherwise
  • Displays convergence warning messages when maxiter is reached
• Enhanced GridSearch() function with convergence handling
  • Automatically excludes non-converged results from optimization
  • Displays warning messages for parameter combinations that failed to converge
  • Returns list of failed settings in output
  • Terminates with error message if all parameter combinations fail to converge
• Improved numerical stability in Biclustering()
  • Implemented conditional pmax() application to avoid unnecessary log-likelihood inflation
  • Applied numerical correction only when NaN/Inf values are detected
• High-performance polychoric correlation computation
  • Implemented C++ acceleration for polychoric correlation calculations
  • Achieved significant speed improvements over R-based implementation
• Improved Array-type plot visualization with enhanced color palette
  • Replaced dull default colors with vibrant, high-contrast color palette
  • Added colorblind-friendly color scheme for better accessibility
  • Binary data (2 categories) now uses black and white for optimal contrast
  • Multi-category data uses enhanced colorblind-accessible palette

exametrika 1.6.2

• Biclustering: Fixed floating-point arithmetic errors causing NaN results

exametrika 1.6.1 on Aug 26, 2025

• Biclustering: Fixed improper handling of missing values

exametrika 1.6.0 on Aug 12, 2025

• Biculustring.norminal is available!
• Biclustering.ordinal is available!
• New function GridSerch() for grid search optimization of model parameters
• Bugfix: Fix output typos(class/rank)
• Added duplicate ID validation to dataFormat()
• Bugfix: Fix ID column detection in dataFormat()
• Bugfix: Fix stanine division error when unable to split data

exameterika 1.5.2 on March 29, 2025

• Bugfix: Fix output typos(TestStatisticsFunction,GRMs)

exameterika 1.5.1 on March 8, 2025

• Field analysis for Biclustering is included in the Biclustering() function
• Class/Rank Reference Vector plot is now available.
• Bug Fix for polychoric correlations

exametrika 1.5.0 on March 5, 2025

• New function GRM is available!

exametrika 1.4.4 on March 3, 2025

• In Exametrika 1.4.1, bug fixes were made.
• In 1.4.2, it became possible to calculate polychoric correlation and polyserial correlation.
• In 1.4.3, item analysis for polytomous items became available.
• In 1.4.4, we renamed “ICC” to “IRF,” although they refer to the same concept (Item Characteristic Curves and Item Response Functions are interchangeable terms). The function will interpret “ICC” input as “IRF” automatically. Additionally, Test Response Function (TRF) output was also made available.

exametrika 1.4.0 on Feb 25, 2025

• New function LRA() now supports rated data
• Hex icon available

exametrika 1.3.0 on Feb 11, 2025

• Added implementation of latent rank model for ordinal scale data

• New function LRA() now supports ordinal response data

  • Added visualization methods for ordinal scale analysis:
    • Score frequency with rank thresholds (ScoreFreq)
    • Score-rank probability heatmap (ScoreRank)
    • Item category boundary reference (ICBR)
    • Item category response profile (ICRP)

• Bug fixes and improvements

• Standardized terminology: unified the usage of “class” and “rank” throughout the package

• Various minor bug fixes

Exametrika 1.2.0 on Jan 30, 2025.

• Improved numerical stability for model estimation
• Bug fixes for log-likelihood calculation

Exametrika 1.1.0 on Oct 30, 2024.

• Added support for polytomous response data

Exametrika 1.0.2 on Aug 17, 2024.

• Bug fix for Issue #12

Exametrika 1.0.1 on July 31, 2024.

• Bug fix for Item Total Correlation
• Bug fix for Fit indices
• New function called ItemStatistics

Exametrika 1.0.0 on June 9, 2024.

• Initial Release

Exametrika 0.11.5 on Mar 30, 2024.

• Bug fix for plot labels for TRP/LRD

Exametrika 0.11.4 on Jan 27, 2024.

• Bug fix for LCD/LRD for IRM

Exametrika 0.11.3 on December 15, 2023.

• Bug fix for item information curve

Exametrika 0.11.2 on October 27, 2023.

• Plot manual update

Exametrika 0.11.1 on October 27, 2023.

• All model tested by testthat environment

Exametrika 0.11.0 on October 26, 2023.

• Added BINET model

Exametrika 0.10.0 on October 18, 2023.

• Added LDB model

Exametrika 0.9.0 on October 17, 2023.

• Added Structure Learning for LDLRA using PBIL
• Added LDLRA model

Exametrika 0.8.1 on October 10, 2023.

• Added Structure Learning for BMN with simple GA and PBIL model

Exametrika 0.8.0 on October 4, 2023.

• Added BNM model

Exametrika 0.7.0 on October 4, 2023.

• Added IRM model

Exametrika 0.6.1

• Added Q3 matrix output to the IRT function