Skip to contents

Changelog

Source: NEWS.md

exametrika 1.13.0

New features

  • Graphical Lasso (Glasso): New function for sparse precision matrix estimation from ordinal item response data. The polychoric correlation matrix is computed internally and the optimal regularization parameter is selected by Extended Bayesian Information Criterion (EBIC; Foygel and Drton 2010). Implements block coordinate descent (Friedman, Hastie, Tibshirani 2008; Algorithm 17.2 of Hastie, Tibshirani, Friedman 2009) with cyclical coordinate descent for the inner lasso step. Warm-starting across the lambda grid accelerates the search.

    Internal helpers glasso_one() (single-lambda solver) and compute_EBIC_glasso() (EBIC computation) are available but not exported.

    Returns a list with theta (selected precision matrix), lambda_opt (selected lambda), ebic_opt, n_edge, and path (data frame of lambda, ebic, n_edge over the search grid).

  • print.exametrika Glasso method: Added a Glasso branch to the shared print.exametrika() dispatcher to summarize the estimated model (optimal lambda, EBIC value, edge count, precision matrix).

  • Chatterjee’s xi correlation (chatterjee_xi, xi_stable, chatterjee_matrix): New family of functions implementing Chatterjee’s (2021) rank-based correlation coefficient. chatterjee_xi() computes the single-shot value with random tie-breaking. xi_stable() averages B replications (default B = 1000) to stabilize against tie-induced variability and returns a list with the mean, standard deviation, standard error, and B. chatterjee_matrix() produces the p x p asymmetric pairwise xi matrix from ordinal data with pairwise-complete handling of missing values; the asymmetry between xi(j, k) and xi(k, j) enables direction detection in graphical-model construction.

Documentation and CRAN check cleanup

  • Roxygen Markdown bracket escapes: Wrapped bracketed expressions inside roxygen blocks of Glasso() and chatterjee_matrix() in backticks (e.g., `[0, 1]`, `[j, k]`, `Q[i, j]`) so that roxygen2 no longer rewrites them as \link{} cross-references. Resolves R CMD check WARNINGs about missing link targets.
  • Glasso() ... documented: Added @param ... to Glasso() so that R CMD check’s “Undocumented arguments” WARNING is cleared.
  • compute_EBIC_glasso() partial-match fix: Replaced determinant(Theta, log = TRUE) with the full argument name logarithm = TRUE, removing the partial-argument-match NOTE.

exametrika 1.12.1

Bug fixes

  • Biclustering EM stability for empty fields/classes: Both Biclustering.nominal() and Biclustering.ordinal() no longer abort with "missing value where TRUE/FALSE needed" when extreme grid configurations (e.g., very small ncls combined with very large nfld) leave fields or classes empty during EM. The two log-likelihood checks inside the EM loop — the relative convergence test and the monotonicity guard — now treat a non-finite test_log_lik as a non-converged exit instead of throwing. Affected cells are returned with converge = FALSE so that GridSearch() skips them automatically when comparing criteria. This also resolves the same crash in Biclustering.rated(), which calls Biclustering.nominal() internally.

exametrika 1.12.0

New features

  • Class-side Confirmatory Biclustering: Biclustering() now accepts a conf_class argument that fixes class memberships during EM. Like conf (which fixes field memberships), conf_class accepts either a vector of class labels (one per respondent) or a 0/1 membership matrix (respondents x classes). When supplied, the class-side E-step is overridden every iteration. For Ranklustering (method = "R"), the neighbour-smoothing step is skipped (smoothed_memb <- clsmemb) since smoothing pre-fixed labels would defeat the purpose of fixing them. Available for binary, ordinal, nominal, and rated data; can be combined with conf to fix both fields and classes simultaneously. Note: rated re-orders classes by correct rate after estimation, so the output class labels may not match the input labels (the individual-to-class mapping is preserved up to relabeling).

    Number of model parameters (nparam) is intentionally not adjusted when conf or conf_class is in effect: only PiFR / BCRM cell probabilities count as parameters in this implementation, and membership matrices are latent posteriors, not parameters.

Bug fixes

  • Confirmatory ordinal Biclustering: field membership now stays fixed during EM: Biclustering.ordinal() was overwriting fldmemb with the E-step estimate every iteration, so the conf/conf_mat argument only affected the initial value. Aligned the implementation with Biclustering.binary() by re-applying fldmemb <- conf_mat immediately after the field-side E-step. With this fix result$FieldEstimated matches the user-supplied assignment exactly.

  • Confirmatory nominal Biclustering: conf argument is now actually honored: Biclustering.nominal() validated length(conf) against NCOL(U), but U is an exametrika list object so NCOL(U) always returned 1. The check rejected every well-formed conf vector with “conf vector size does NOT match with data.”, making confirmatory nominal Biclustering unreachable since v1.10.0. Replaced with NCOL(U$Q) (and the matrix-form NROW(conf) check, plus the conf_mat allocation, in the same way).

  • Confirmatory ordinal Biclustering: conf length check actually validates input: same NCOL(U) issue as nominal, but in Biclustering.ordinal(). Fixed by switching to NCOL(U$Q).

  • Confirmatory binary Biclustering: conf size checks now reference the formatted response matrix explicitly: Biclustering.binary() happened to work because U is rebound to tmp$U * tmp$Z before the conf block, so NCOL(U) returned the item count. Switched to NCOL(tmp$U) for consistency with the ordinal/nominal fixes and to make the intent obvious to readers.

  • Confirmatory Biclustering: matrix-form conf is no longer silently dropped: when conf was supplied as a membership matrix (items x fields) instead of a vector, all three implementations (binary, ordinal, nominal) validated the matrix but never copied it into conf_mat, so the subsequent nfld <- NCOL(conf_mat) failed with object 'conf_mat' not found. Added conf_mat <- as.matrix(conf) in the matrix branch.

  • Biclustering.ordinal() now honors the maxiter argument: The inner EM iteration cap was hardcoded to 100 (maxemt <- 100) and ignored the user-supplied maxiter. This mirrors the bug that was fixed for Biclustering.nominal() in 1.11.0. Callers that pass a larger maxiter (e.g. 2000 in Monte Carlo studies) now actually use that ceiling.

  • GRM() fit indices no longer return NaN for moderate or larger item counts: The benchmark model used const <- exp(-nitems * 100) as a log-domain epsilon inside sum(cat_counts * log(cat_probs + const)). For about 8 or more items this expression underflows to exactly 0 in IEEE-754 double precision, so log(0) = -Inf propagates through 0 * -Inf = NaN and poisons every downstream index (model_Chi_sq, NFI, CFI, RMSEA, AIC, CAIC, BIC). The benchmark and null loops now skip zero-count categories explicitly, removing the need for an additive epsilon.

  • GRM() degrees of freedom were computed incorrectly: The model df was set to n_pattern * (ncat - 1) + 1 and the null df to n_pattern * (ncat - 1), which is neither (# bench params) - (# model params) nor (# bench params) - (# null params). The inflated df then clamped CFI, TLI, IFI, and RMSEA to zero whenever chi^2 < df (and the previous df_A > df_B inequality was the wrong direction to begin with). The convention now matches Biclustering.ordinal():

    • bench_nparam_j = n_pattern * (ncat_j - 1)
    • null_nparam_j = ncat_j - 1
    • model_nparam_j = ncat_j (one slope plus ncat_j - 1 thresholds)
    • df_A_j = bench_nparam_j - model_nparam_j
    • df_B_j = bench_nparam_j - null_nparam_j

  • GRM() now accepts any integer-coded ordinal responses, not just 1..K: Category counts were derived from apply(dat, 2, max), which assumes responses are already 1-indexed. Data coded from 0 (e.g. 0..3) or with gaps (e.g. 1, 2, 4) undercounted ncat[j] by one or more and then indexed grm_prob()[resp] / v[resp] out of range, producing either an outright invalid subscript type 'list' error (on J15S3810-style data) or silently truncated threshold columns. Each item’s responses are now remapped to contiguous 1..K codes via match() against the sorted unique values on entry, with ncat[j] derived from the mapped levels; both the R model-fit blocks and the C++ log-likelihood receive 1-based input regardless of the user’s coding.

  • GridSearch() now tolerates per-cell fit errors: Previously, a single Biclustering() (or LCA() / LRA()) call that raised an error at a grid corner (for example, empty-cluster edge cases at large ncls/nfld with small nobs/nitems) would propagate out of GridSearch() and abort the entire grid. The call to the underlying analysis function is now wrapped in tryCatch, and errors are handled the same way as non-convergence: the cell is marked NA in the index matrix and the (ncls, nfld) pair is recorded in failed_settings. GridSearch() still raises only when all grid cells fail, preserving the existing “all-failed” error.

Performance

  • C++ implementation of the IRM Gibbs sampler core: The collapsed Gibbs sampler shared by Biclustering_IRM.nominal(), Biclustering_IRM.ordinal(), and Biclustering_IRM.rated() is now implemented in C++ via Rcpp (src/irm_gibbs_core.cpp). The R reference implementation in R/00_IRM_Gibbs_CORE.R is preserved behind irm_gibbs_core(..., use_cpp = FALSE) for cross-checking.

    • Numerical reproducibility: With the same set.seed(), the C++ path produces output bit-identical to the R reference. RNG calls are routed through R-level sample.int() and rmultinom() (via Rcpp::Function) so the unif_rand() consumption order matches base R exactly. The C-level entry points (Rcpp::sample, R::rmultinom) consume RNG differently from base R and were explicitly avoided.
    • Wall-clock: roughly 4x speedup on the inner Gibbs loop on the bundled test datasets (J20S600 nominal: 82 to 20 ms/iter; J35S500 ordinal: 76 to 19 ms/iter). The end-to-end Biclustering_IRM() call also benefits proportionally on larger iteration counts (e.g. simulation runs).
    • No new dependencies: Rcpp is already in LinkingTo:; the implementation uses only Rcpp::Function and <vector>/<cmath>.

  • Vectorized EM hot path in Biclustering.ordinal(): The per-iteration cost of the ordinal EM loop has been reduced by rewriting five hot spots in base R without introducing new package dependencies.

    • Replaced apply(Ufcq_prior, c(1, 2), function(x) rev(cumsum(rev(x)))) with an in-place reverse cumulative sum loop over the category axis. The previous form dispatched nfld * ncls R-level calls per EM iteration; the new form performs maxQ - 1 vectorized array additions.
    • Replaced apply(X, 1, min) and apply(X, 1, max) with do.call(pmin.int, as.data.frame(X)) / do.call(pmax.int, as.data.frame(X)). These compute the row-wise reduction in a small number of C-level calls rather than one R-level call per row. apply(X, 1, which.max) was similarly replaced by max.col(X, ties.method = "first").
    • Precomputed log(BBRM[,,q] - BBRM[,,q+1] + const) once per EM iteration instead of independently in the class-side and field-side E-steps for every q.
    • Precomputed Z * Uq[,,q] once per fit (call it ZU) via column-major recycling (Uq * as.vector(Z)), replacing about eight separate elementwise products per EM iteration and reusing the same array in the post-EM fit-index blocks. The replicate(maxQ, Z) allocation that produced Zrep has been removed as a byproduct.
    • Replaced apply(X, c(1, 2), sum) and apply(X, c(2, 3), sum) on 3-D arrays with rowSums(X, dims = 2) / colSums(X, dims = 1), and apply(M, 2, sum) on matrices with colSums(M).

    Output is bit-identical to 1.11.0 on every tested configuration (max(abs(old - new)) == 0 for BCRM, BBRM, ClassMembership, FieldMembership, TestFitIndices, and the full EM trajectory). Single-fit wall-clock on typical sizes (ncls, nfld up to 20 by 15 on J35S500) improves by roughly 1.2 to 1.3 times; the per-EM-iteration speedup compounds across GridSearch() grids.

  • Vectorized 3-D apply reductions in Biclustering.nominal(): The same rowSums/colSums(X, dims = ...) substitutions were applied to the nominal M-step and null-model blocks, and Zrep was eliminated in favor of the ZU precomputation. Output is bit-identical to 1.11.0.

  • Vectorized Uq one-hot encoding in Biclustering.ordinal() and Biclustering.nominal(): The construction of the one-hot response array Uq[i, j, tmp$Q[i,j]] = 1 previously used a nobs * nitems nested R loop. It now writes all ones in one C-level matrix-index assignment, restricted to non-missing cells (tmp$Z == 1). Single-fit wall-clock on the validation matrix improves by 1.2-5.3x over 1.11.0 (up to 5x on small cases where the loop overhead dominated). Output is bit-identical to 1.11.0 at every downstream location (the missing entries of the old Uq were never read because every consumer applies the tmp$Z mask).

Output structure

  • Biclustering.ordinal() now returns SmoothedMembership: The smoothed (Ranklustering-filtered) class membership matrix is now part of the return value, mirroring Biclustering.binary(). This fills a long-standing gap (only the binary form previously exposed it) and makes Ranklustering with conf_class introspectable: callers can verify that the smoothing step is correctly skipped when class labels are pre-fixed (SmoothedMembership equals ClassMembership in that case).

Tests

Notes

  • No changes to package Imports or Depends. The new C++ Gibbs sampler uses Rcpp, which was already declared in LinkingTo:.
  • No new exported functions. The new conf_class argument on Biclustering() is additive and defaults to NULL, preserving the previous behavior for all existing callers.

exametrika 1.11.0

CRAN release: 2026-04-15

New Features

  • Biclustering_IRM.rated(): Rated IRM (Infinite Relational Model for multiple-choice data): Performs IRM-based biclustering for rated data (items with correct answers and multiple response categories). Internally calls Biclustering_IRM.nominal() for estimation via Dirichlet-Multinomial collapsed Gibbs sampler, then post-processes the results: classes are sorted by correct response rate (ranklustering), and two layers of fit indices are reported — binary (item correct/incorrect, with full benchmark model including CFI/RMSEA) and nominal (category-level, AIC/BIC/CAIC only). Returns TestFitIndices (binary, default) and TestFitIndices_nominal (nominal) in the output.

  • DistractorAnalysis(): Distractor analysis for rated models: S3 generic function for analyzing response category distributions by rank/class. Computes observed frequency tables, proportion tables, chi-square tests against chance level, and Cramer’s V effect sizes for each item-by-rank cell. Works with LRA.rated and Biclustering.rated / Biclustering_IRM.rated results. For Biclustering models, output is grouped by field. Supports items and ranks filtering in both print() and plot() methods.

New Data

  • J21S300: New rated sample dataset (21 items, 300 students, 4 response categories) for testing rated Biclustering and IRM models. Smaller and faster than J35S5000 (35 items, 5000 students).

Bug Fixes

  • Fixed maxiter parameter ignored in Biclustering.nominal(): The maxiter parameter was accepted but internally overridden by a hardcoded maxemt <- 100. Now correctly uses maxiter as the iteration limit.

  • Fixed spurious “No ID column detected” message in Biclustering.rated() and Biclustering_IRM.rated(): The crr() function was called with a raw matrix (tmp$Z * tmp$U) instead of an exametrika object, triggering an unnecessary dataFormat() call. Now creates a temporary binary-typed exametrika object to avoid the message.

exametrika 1.10.2

New Features

  • Biclustering.rated(): Rated (multiple-choice) Biclustering: Performs biclustering for rated data (items with correct answers and multiple response categories). Internally calls Biclustering.nominal() for estimation, then post-processes the results: classes are sorted by correct response rate (ranklustering), and two layers of fit indices are reported — binary (item correct/incorrect, with full benchmark model) and nominal (category-level, AIC/BIC/CAIC only). The binary layer uses item-level correct response rates per class without field constraints (nparam = nitems * ncls). Supports both Biclustering (method = "B") and Ranklustering (method = "R") modes. Returns TestFitIndices (binary) and TestFitIndices_nominal (nominal) in the output. Added 16 tests for rated Biclustering using J35S5000.

Improvements

  • Renamed ItemFRP to quasiFRP in Biclustering.rated() output: The item-level correct response rate matrix (J x C) is computed without field constraints because the field assignments come from nominal analysis and have different meaning in the binary context. The name quasiFRP (quasi Field Reference Profile) clarifies this distinction from the standard field-constrained FRP.

Bug Fixes

  • Fixed subscript out of bounds in nominal/ordinal Biclustering with large nfld: The initial field assignment ceiling(1:nitems / (nitems / nfld)) could exceed nfld due to floating-point rounding. Added pmin(..., nfld) clamping, consistent with the binary Biclustering fix already in place.
  • Fixed redundant dataFormat() call in Biclustering.rated(): The function was unnecessarily re-calling dataFormat() on already-formatted data, causing repeated “No ID column detected” messages during GridSearch(). Now reuses the existing exametrika object directly.
  • Fixed Array plot for polytomous Biclustering (rated/nominal/ordinal): Array plots for polytomous Biclustering models were rendered in black and white because x$U (binary correctness matrix) was used instead of x$Q (polytomous responses). Now correctly uses x$Q for polytomous models.
  • Fixed FCRP plot invalid graphics state error: par(mfrow=...) and layout() conflicted when plotting FCRP/FCBR. Now skips par(mfrow=...) for plot types that use layout() internally.
  • Improved id parameter validation in dataFormat(): When a vector (e.g., id = tmp$ID) was passed instead of a column number, the error message was cryptic (the condition has length > 1). Now validates that id is a single integer and provides a clear error message.
  • Fixed nominal Biclustering/IRM fit indices: Removed benchmark (saturated) model for nominal data. With many items and categories, every examinee has a unique response pattern, making the benchmark model trivially saturated (ell_B = 0) and chi-square based indices (NFI, RFI, IFI, TLI, CFI, RMSEA) meaningless. These indices are now reported as NA for nominal data. Information criteria (AIC, BIC, CAIC) are computed directly from the model log-likelihood and remain available. Also fixed a length(benchGroup) → length(unique(benchGroup)) bug in Biclustering.nominal() (the unique() call was missing).

Improvements

  • Added empty field warning for all Biclustering models: When some fields have no items assigned (e.g., due to specifying too many fields), a warning message is now emitted suggesting to reduce nfld. Affects Biclustering() (binary/ordinal/nominal), Biclustering_IRM() (binary/ordinal/nominal).
  • Renamed field to fld in Biclustering_IRM.binary(): Internal variable name changed from field to fld for consistency with other Biclustering models. No change to the public API (FieldEstimated output name is unchanged).

Documentation Fix

  • Fixed J35S5000 UseCase in data format tables: Corrected from “Nominal LRA” to “Rated LRA” in getting-started vignette, and from “名義LRA” to “評定LRA” in Japanese guide. J35S5000 is a rated (multiple-choice with correct answers) dataset, not nominal.
  • Fixed J15S3810 roxygen documentation: The @format field incorrectly stated “nominal responses” but the dataset is ordinal (response.type=ordinal). Also fixed “A ordinal” to “An ordinal” in @description.

exametrika 1.10.1

CRAN release: 2026-03-19

Vignette Build Time Reduction

  • Reduced vignette build time from ~46 minutes to ~5 minutes: Added eval=FALSE to computationally intensive code chunks in vignettes (GridSearch, IRM, LDLRA, LDLRA_PBIL, LDB, BINET, ordinal/nominal Biclustering, ordinal/rated LRA). The Japanese guide (guide-ja) now uses eval=FALSE for all model estimation chunks to avoid duplicating computation from English vignettes. Full rendered output is available on the pkgdown site.

exametrika 1.10.0

Bug Fixes

CAIC (Consistent AIC) Formula Correction

  • Fixed CAIC formula to match Bozdogan (1987) original definition: The CAIC penalty term was log(n + 1) but should be log(n) + 1 per Bozdogan (1987, Psychometrika, 52(3), p.358, Proposition 2, Eq.44). The original Mathematica implementation had this error (Log[nobs + 1]), and the R port inherited it. Both the R version (R/00_ModelFitModule.R) and the Mathematica version (develop/mtmk15forVer13/mod/Module_ModelFit.nb) have been corrected. The numerical difference is approximately 1 (constant), but the corrected formula now matches the published definition: CAIC(k) = -2 log L + k * (log(n) + 1). This affects all models that compute fit indices: IRT, LCA, LRA (binary/ordinal/rated), Biclustering (binary/ordinal/nominal), IRM, BNM, LDLRA, LDB, BINET, and GRM. All 873 tests pass with the corrected formula.

GRM Example Fix

  • Changed GRM examples from \donttest to \dontrun: GRM’s multi-panel plot examples caused “invalid graphics state” errors in non-interactive environments (pkgdown, CI). Changed to \dontrun to prevent build failures.

dataFormat Robustness Improvements

  • Fixed auto-detection ignoring CA parameter: When CA (correct answer vector) was provided but response.type was not explicitly specified, dataFormat() incorrectly classified the data as "ordinal" instead of "rated". This caused $U (binary scoring matrix) to be NULL. The auto-detection now checks for CA before ordinal/nominal detection: binary → rated (if CA provided) → ordinal → nominal.
  • Fixed id parameter not working for column 1: Changed id default from 1 to NULL. Previously, id = 1 (both default and explicit) triggered auto-detection heuristics, making it impossible to explicitly specify the first column as the ID column. Now id = NULL triggers auto-detection, and any numeric value (including 1) forces that column to be used as ID.
  • Improved ID auto-detection for consecutive integers: Simplified the first-column heuristic to treat unique consecutive integers (e.g., 1:N) as ID regardless of whether they also look like valid response values. Previously, 1:10 was misclassified as response data because looks_like_response_data() returned TRUE.
  • Fixed missing values in rated U matrix: When the response matrix contained missing values (-1), the binary scoring matrix U incorrectly scored them as 0 (incorrect) instead of -1 (missing). Now U[i,j] = -1 when Q[i,j] = -1.
  • Fixed drop=FALSE missing in item exclusion: When items were excluded due to invalid variance (e.g., containing Inf), the column subsetting response.matrix[, mask] dropped matrix dimensions if only one item remained, causing a crash. Added drop = FALSE.
  • Added diagnostic messages: dataFormat() now reports via message() when it detects problematic data:
    • Items with all missing values
    • Items with zero variance (constant response values)
    • Students with all missing responses

BINET g_list / adj_list Input Path Fix

  • Fixed g_csv variable undefined error when using g_list or adj_list input: BINET() crashed with an undefined variable error at the graph construction step when the DAG was specified via g_list or adj_list parameters. The internal variable g_csv (used to build the integrated graph object all_g) was only defined in the adj_file code path. Added logic to reconstruct g_csv from adj_list for the g_list and adj_list input paths, ensuring all_g is correctly built with Field edge attributes regardless of input method.
  • Fixed g_list / adj_list length validation: The length check for g_list and adj_list incorrectly compared against ncls (number of classes) instead of nfld (number of fields). In BINET, each element of adj_list represents the DAG structure at a specific field, so the list length should equal nfld. Also fixed g_list type check from g_list[[1]] (always checking the first element) to g_list[[j]] (checking each element).

Biclustering_IRM.binary S3 Method Consistency Fix

  • Added ... to Biclustering_IRM.binary() signature: The S3 generic Biclustering_IRM(U, ...) requires all methods to include ... in their formal arguments. The .binary method was missing it, causing an R CMD check WARNING. Added ... to match the generic and the .nominal/.ordinal methods.

Biclustering_IRM msg Field Fix

  • Fixed IRM msg field to correctly distinguish Rank vs Class models: Binary IRM and Ordinal IRM are Ranklustering models (ordered latent classes), so msg = "Rank" is correct. Nominal IRM has no Ranklustering concept (unordered latent classes), so msg = "Class" is correct. Previously all three methods had inconsistent values; now Biclustering_IRM.binary and Biclustering_IRM.ordinal return msg = "Rank", while Biclustering_IRM.nominal returns msg = "Class". Also updated internal column names of cls01 and FRP from "Class" to "Rank" for binary/ordinal IRM. The shared Gibbs core (irm_gibbs_core()) output column names were also updated.

Biclustering_IRM.binary Missing LRD Alias

  • Added LRD (Latent Rank Distribution) alias to Biclustering_IRM.binary() return value: The binary IRM was the only Biclustering model that did not include LRD in its return value (it only had LCD). Other Biclustering models (binary Biclustering, nominal IRM, ordinal IRM) all return both LRD and LCD. Added LRD = clsdist as an alias for LCD to ensure consistency across all Biclustering-family models. This improves interoperability with downstream packages (ggExametrika) that look up LRD when msg == "Rank".

Biclustering_IRM Seed Default

  • Reverted Biclustering_IRM() seed default back to 123: Ensures reproducibility by default.

Biclustering_IRM t(apply()) Dimension Drop Fix

  • Fixed t(apply(log_S, 1, irm_log_to_prob)) dimension drop in Nominal/Ordinal IRM: When the Gibbs sampler converged to ncls=1 or nfld=1, apply() on a single-column matrix returned a vector instead of a matrix, causing t() to produce a 1-row matrix instead of an N-row matrix. This led to incorrect class/field membership assignment. Replaced all 6 instances of t(apply(mat, 1, fun)) with explicit for-loops in both Biclustering_IRM.nominal() (3 locations: EM E-step, final class membership, final field membership) and Biclustering_IRM.ordinal() (3 locations: same). Consistent with the project’s apply() caution policy.

Biclustering_IRM Log-Probability Normalization Fix

  • Fixed log-to-probability conversion in Biclustering_IRM() Gibbs sampler: Changed exp(ptab - min(ptab)) to exp(ptab - max(ptab)) for numerical stability. The previous implementation subtracted the minimum log-probability, which could cause overflow (exp(large positive) = Inf) when the range of log-probabilities was large, resulting in NaN after normalization. Subtracting the maximum ensures the largest value becomes exp(0) = 1 and all others underflow harmlessly to near-zero values.

Biclustering.nominal Model Fit Fix

  • Fixed Biclustering.nominal() using stale log-likelihood in model fit indices: When the EM algorithm exited due to log-likelihood decrease, BCRM was correctly reverted to the previous iteration’s value, but test_log_lik was not. The model fit section recalculated the correct log-likelihood as testell, but chi_A, model_log_like, log_lik, and LogLik all referenced the stale test_log_lik instead of testell. Also removed a duplicated model fit code block (copy-paste error).

J20S400 Dataset Fix

  • Fixed J20S400 response type from nominal to binary: The J20S400.rda dataset was incorrectly stored with response.type = "nominal" despite being a binary (0/1) dataset with -99 as missing values. Missing values were not properly handled (Z was all 1s, Q contained raw -99 values). Regenerated from the original CSV source (develop/sampleData/J20S400.csv) with dataFormat(..., na = -99), now correctly typed as binary with 86 missing values properly masked in Z.

New Features

Polytomous IRM (Biclustering_IRM.nominal / Biclustering_IRM.ordinal)

  • New Biclustering_IRM.nominal() for nominal/polytomous data: Extends the Infinite Relational Model (IRM) from binary to nominal scale data using a Dirichlet-Multinomial collapsed Gibbs sampler. The Chinese Restaurant Process (CRP) automatically determines the optimal number of classes and fields. After the Gibbs sampling phase, small classes are consolidated and refined with an EM algorithm. The Dirichlet prior concentration parameter alpha controls smoothing of category probabilities.
  • New Biclustering_IRM.ordinal() for ordinal/polytomous data: Extends IRM to ordinal scale data. Shares the same Dirichlet-Multinomial collapsed Gibbs sampler as nominal IRM (Phase 1), then applies ordinal-specific EM refinement (Phase 2) with cumulative normalization to enforce monotonic category boundaries. The mic parameter (default TRUE) enforces monotone increasing class ordering by expected score sum. Reports BFRP (Bicluster Field Reference Profile), FRPIndex, TRP, and Strongly/Weakly Ordinal Alignment Conditions (SOACflg/WOACflg).
  • Biclustering_IRM() is now an S3 generic: Dispatches to Biclustering_IRM.binary (existing binary IRM), Biclustering_IRM.nominal (new), and Biclustering_IRM.ordinal (new). Raw data is automatically formatted and dispatched based on response.type.
  • Shared Gibbs sampler core: The collapsed Gibbs sampler has been extracted into a shared internal function irm_gibbs_core() (in R/00_IRM_Gibbs_CORE.R), used by both nominal and ordinal IRM. Helper functions (irm_calc_Ufcq, irm_lmvbeta, irm_log_to_prob, irm_bic_calc, etc.) are also shared.
  • Performance optimization: The Gibbs sampler uses differential updates for the sufficient statistics array U_fcq, computing only the contribution of the target student/item rather than recalculating the full array at each step.

Confirmatory LCA/LRA (Test Equating)

  • LCA() and LRA() now support a conf parameter for confirmatory analysis: The conf argument accepts an IRP matrix (ncls/nrank x testlength) where non-NA values are held fixed throughout EM estimation and NA values are freely estimated. This enables test equating scenarios where anchor items retain their known IRPs while new items are calibrated against them.
  • Label-based item matching: When conf has column names, items are matched by label rather than by position. This allows conf to contain a subset of items (anchor items only) or items in a different order than the data. Items in the data but not in conf are automatically set to freely estimated. Unknown labels in conf produce an informative error.
  • Works with both GTM and SOM methods for LRA.

Internal Refactoring: SOM Estimation

  • Extracted SOM estimation into somclus() internal function: The Self-Organizing Maps estimation code previously inlined in LRA.binary() (~100 lines) has been extracted into a standalone internal function somclus() in 00_EMclus.R. This parallels emclus() (GTM/EM) and returns the same structure. Also fixed a pre-existing typo (h_cout → h_count) and another (oldsBIC → oldBIC).

Test Suite Modernization

  • Complete migration from Excel to CSV fixtures: Removed all 14 legacy test files that depended on tidyverse and readxl for reading Excel-based Mathematica reference data. Replaced with 24 modern test files using base R read.csv() and test_path() to load CSV fixtures from tests/testthat/fixtures/mathematica_reference/. The new test suite has zero external package dependencies beyond testthat.
  • Removed readxl/tidyverse from test dependencies: DESCRIPTION Suggests no longer requires any packages beyond knitr, rmarkdown, and testthat.
  • Fixture file reorganization: Shortened overly long CSV fixture filenames to comply with CRAN’s 100-byte portable path requirement.
  • Test coverage: 26 test files, 321 test blocks, covering all models (CTT, IRT 2PL/3PL/4PL, LCA, LRA binary/ordinal/nominal, Biclustering binary/ordinal/nominal, IRM binary/nominal/ordinal, BNM, LDLRA, LDB, BINET, GRM, GridSearch, dataFormat, polychoric correlation, scoring, student/test analysis, confirmatory LCA/LRA). 85 Mathematica reference CSV files for cross-validation.
  • Added Nominal IRM tests (test-irm-nominal.R): 18 test blocks for Biclustering_IRM.nominal() using J20S600 data — basic execution, dimensions, FRP validity, membership, fit indices, backward compatibility, seed reproducibility, alpha validation.
  • Added Ordinal IRM tests (test-irm-ordinal.R): 25 test blocks for Biclustering_IRM.ordinal() using J35S500 data — basic execution, dimensions, FRP validity, expected scores, TRP, BFRP, FRPIndex, SOAC/WOAC flags, mic parameter, fit indices, backward compatibility, seed reproducibility, alpha validation.

Documentation

  • TestStatistics example: Added stem-and-leaf plot example using stem(nrs(dataFormat(J15S500))) to demonstrate score distribution visualization.

Internal Improvements

  • pkgdown migration: Migrated documentation site from Jekyll (main/docs) to pkgdown (gh-pages branch via GitHub Actions).
  • CI/CD: Added GitHub Actions workflows for automated R CMD check, test coverage reporting, and pkgdown site deployment. Removed Codecov upload step from test-coverage workflow.
  • .Rbuildignore cleanup: Removed ^tests$ and ^inst$ entries that were incorrectly excluding tests and vignettes from the built package.

exametrika 1.9.0

Bug Fixes

GridSearch index Parameter Fixes

  • Index alias support: GridSearch() now accepts common aliases for fit indices. "loglik", "log_lik", "LogLik", and "LL" are mapped to "model_log_like". "Chi_sq" and "chi_sq" are mapped to "model_Chi_sq". Previously, using these aliases caused silent NULL extraction and eventual errors.
  • Early validation: Invalid index names are now caught immediately at the start of GridSearch(), before the computationally expensive grid search loop runs. The error message lists all valid options including available aliases.
  • Log-likelihood optimization direction: model_log_like is now correctly treated as a maximization target (larger log-likelihood = better fit). Previously, it was incorrectly placed in the minimization group, which would have selected the worst-fitting model.

Test Tolerance Fix

  • Q3 matrix test: Changed the 2PL Q3 matrix test from relative tolerance (tolerance = 1e-2 in expect_equal) to absolute difference comparison (threshold 0.005). Q3 residual correlations include values near zero where relative tolerance comparisons are unreliable.

NAMESPACE Fix

  • Missing imports: Added layout and plot.new from graphics to NAMESPACE. These functions are used by the legend strip layout helpers for polytomous Biclustering plots.

LRA.ordinal / LRA.rated Category Computation Fix

  • Fixed apply(U$Q, 2, unique) returning matrix instead of list: When all items have the same number of response categories (e.g., all 5-point Likert), apply() returns a matrix rather than a list. The subsequent lapply() then iterates over individual elements instead of per-column vectors, causing ncat to be a vector of 1s and crashing the algorithm. Replaced with lapply(seq_len(nitems), function(j) sort(unique(U$Q[, j]))) which always returns a proper list. Same fix applied to catfreq999 computation. Both LRA.ordinal and LRA.rated were affected.

GRM ItemFitIndices Computation Fix

  • Fixed apply(tmp$Q, 2, table) returning matrix instead of list: Same class of bug as the LRA.ordinal/LRA.rated fix above. In GRM(), the null model log-likelihood computation used apply(tmp$Q, 2, table) to compute per-item category frequencies. When all items have the same number of response categories (e.g., all 5-point Likert), apply() returns a matrix instead of a list, causing response_list[[j]] to extract a single number rather than the full frequency table. This produced incorrect null_log_like values and consequently wrong chi-square statistics, RMSEA, TLI, and CFI in ItemFitIndices. Replaced with lapply(seq_len(nitems), function(j) table(tmp$Q[, j])) which always returns a proper list.

LRA.ordinal Mixed Category Count Validation

  • Added input validation for mixed category counts: LRA.ordinal() now raises an informative error when items have different numbers of response categories (e.g., some items with 3 categories and others with 5). The internal matrix algebra uses fixed-stride indexing that assumes uniform category counts. The error message suggests alternatives (LRA.rated, Biclustering.ordinal) that support mixed category counts via list-based designs.

LCA/LRA FRP Plot Type Removal

  • Removed “FRP” from valid plot types for LCA and LRA: Field Reference Profile (FRP) requires a field structure (item grouping), which LCA and LRA do not have. Previously, plot(lca_result, type = "FRP") passed validation but failed at runtime because the $FRP field does not exist in LCA/LRA return values. Now properly rejected with an informative error message at the validation stage.

New Features

New Sample Datasets for Polytomous Biclustering

  • J35S500: Simulated ordinal dataset (500 students, 35 items, 5 categories) with a cumulative staircase pattern (5 latent classes, 5 fields). Contains approximately 0.5% missing values.
  • J20S600: Simulated nominal dataset (600 students, 20 items, 4 categories) with a cyclic category preference pattern (5 latent classes, 4 fields). Contains approximately 0.5% missing values.

New Plot Types for Polytomous Biclustering

  • FRP (Field Reference Profile): Expected score line plot per field, with stat parameter supporting "mean" (default), "median", and "mode".
  • FCRP (Field Category Response Profile): Category probability plot per field, with style parameter supporting "line" (default) and "bar" (stacked bar chart).
  • FCBR (Field Cumulative Boundary Reference): Boundary probability plot per field (ordinal Biclustering only).
  • ScoreField: Heatmap of expected scores across fields and latent classes/ranks.
  • RRV (Rank Reference Vector): Transposed view with fields on x-axis and expected scores on y-axis, with stat parameter.

FRPIndex for Ordinal Biclustering

  • Ordinal Biclustering now computes FRPIndex (Field Reference Profile indices) including location parameters (Alpha, Beta), slope parameters (A, B), and monotonicity indices (Gamma, C).

New Parameters for plot.exametrika()

  • stat: Controls the summary statistic for FRP and RRV plots ("mean", "median", "mode").
  • style: Controls the display style for FCRP plots ("line", "bar").

Array Plot Improvements

  • Missing value display: Array plots now display missing values in a distinct color. Binary data uses gray (#808080) to distinguish from white (incorrect) and black (correct). Polytomous data uses black (#000000) to distinguish from the category color palette.
  • Ordinal Biclustering: print() now displays FRPIndex (Field Reference Profile Indices) with a note that the values are based on normalized expected scores (E[score]-1)/(maxQ-1).

Documentation Improvements

  • FRPIndex: Expanded documentation for the 6 profile shape indices (Alpha, A, Beta, B, Gamma, C) in ?Biclustering, including detailed definitions and polytomous adaptation logic.

Return Value Structure Unification

Systematic unification of return value structures across all analysis functions for consistency and interoperability.

snake_case Field Names Extended to All Functions
  • LDLRA: Added n_class (retaining Nclass for backward compatibility)
  • LDB: Added n_rank, n_field (retaining Nrank, Nfield)
  • BINET: Added n_class, n_field (retaining Nclass, Nfield)
  • Biclustering.nominal: Added n_class, n_field, n_cycle (retaining Nclass, Nfield, N_Cycle)
  • Biclustering.ordinal: Added n_class, n_field, n_cycle (retaining Nclass, Nfield, N_Cycle)
  • Biclustering_IRM: Added n_cycle, N_Cycle (retaining em_cycle, EM_Cycle as IRM-specific aliases)
Top-Level log_lik Added to All Functions
  • All analysis functions now consistently provide log_lik at the top level of the return object, matching TestFitIndices$model_log_like.
  • Functions updated: IRT, LCA, LRA.binary, BNM, LDLRA, LDB, BINET, Biclustering.binary, Biclustering_IRM
TestFitIndices Structure Unified
  • GRM, Biclustering.nominal, Biclustering.ordinal: TestFitIndices now uses the full 16-field structure with ModelFit class, matching the format used by IRT/LCA/LRA and other functions. Previously these used bare calcFitIndices() output (9 fields, no class). Added fields: model_log_like, bench_log_like, null_log_like, model_Chi_sq, null_Chi_sq, model_df, null_df.
  • GRM ItemFitIndices: Also unified to the full 16-field structure with ModelFit class.
  • BINET: TestFitIndices added as primary name for multigroup fit indices (previously only MG_FitIndices). MG_FitIndices retained as backward-compatible alias. SM_FitIndices (saturated model) remains unchanged.
Students Matrix Enhanced
  • Biclustering.nominal: Added Estimate column (most probable class assignment) to the Students matrix.
  • Biclustering.ordinal: Added Estimate column (most probable class assignment) to the Students matrix.
Other Structural Improvements
  • Biclustering_IRM: Added FRPIndex (Field Reference Profile indices) for consistency with Biclustering.binary and LDB.
  • LDB: Fixed TRP from matrix(1×ncls) to numeric vector, consistent with all other functions.
  • GridSearch: Added class = c("exametrika", "GridSearch") to return value for method dispatch support.

Bug Fixes

  • LCA msg field assignment: Fixed msg <- "Class" to msg = "Class" inside structure() call (line 150 of 05_LCA.R). The <- operator was being interpreted as a standalone assignment rather than a named list element, causing the msg field name to be empty.
  • RMP/CMP single student plot error: Fixed dimension drop error when plotting RMP or CMP for a single student (e.g., plot(r, type="RMP", students=1)). Added drop = FALSE to prevent matrix-to-vector coercion when extracting a single row from the Students matrix.
  • LRA.ordinal / LRA.rated TestFitIndices log-likelihood fields: Fixed null_log_like which incorrectly stored the saturated model log-likelihood (log_lik_satu) instead of the null model log-likelihood (sum(null_itemll)). Added missing bench_log_like field containing the saturated model log-likelihood (sum(satu_itemll2)). Note: the chi-square-based fit indices (NFI, CFI, TLI, RMSEA, AIC, BIC, etc.) were always computed correctly; only the stored log-likelihood labels were affected.
  • LRA.ordinal / LRA.rated FitIndices structure: Unified TestFitIndices and ItemFitIndices to the standard 16-field structure with ModelFit class (c("exametrika", "ModelFit")), matching all other analysis functions. Added model_log_like, bench_log_like, null_log_like to ItemFitIndices. Removed ScoreRankCorr / RankQuantCorr from TestFitIndices (already available at the top level of the return object).
  • LRA.ordinal / LRA.rated test updates: Updated test-12OLR.R and test-13NLR.R to handle the new ModelFit class (add unclass() before as.data.frame()) and adjusted column/index references to match the unified 16-field structure.

Plot Layout Improvements

  • Legend strip layout: Moved per-panel legends to a shared legend strip below the plot area for FCRP (line/bar), FCBR, GRM IRF, and IRT overlay (IRF/IIF) plots. Uses layout() with a thin dedicated row (height ratio 0.2) to reduce visual clutter in data panels. Added setup_legend_layout() and draw_legend_strip() internal helper functions.
  • FCBR reference line: Added P(Q>=1)=1.0 reference line at the top of FCBR plots for visual completeness.

exametrika 1.8.1

CRAN release: 2026-02-17

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

CRAN release: 2025-12-10

Naming Convention Improvements

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

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

CRAN release: 2025-11-22

  • 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

CRAN release: 2025-11-06

  • 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

CRAN release: 2025-09-30

  • 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

CRAN release: 2025-08-27

  • Biclustering: Fixed improper handling of missing values

exametrika 1.6.0 on Aug 12, 2025

CRAN release: 2025-08-19

  • 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

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.

CRAN release: 2025-01-31

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

Exametrika 1.1.0 on Oct 30, 2024.

CRAN release: 2024-11-22

  • 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