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Changelog

Source: NEWS.md

mathmodels 0.0.11

  • New function ts_arimax(): Fits ARIMA models with exogenous regressors (ARIMAX). Supports both automatic order selection (forecast::auto.arima()) and manual specification (forecast::Arima()), consistent with the existing ts_sarima() interface.
  • Updated ts_forecast(): Added newxreg parameter for forecasting ARIMAX models with future exogenous regressor values.
  • Improved compute_mf() / compute_mf_funs(): Added .builder parameter supporting Gaussian ("gauss") and sigmoid ("sigmoid") membership functions in addition to the default triangular/trapezoidal ("tri"). Custom builder functions are also accepted. Renamed thresholds parameter to knots for clarity. Fixed defuzzify() floating-point comparison in max_membership method.

Interpolation & curve fitting toolkit (new)

  • Interpolation (interp_fit.R):
  • Curve fitting (interp_fit.R):
    • poly_fit(): Polynomial regression.
    • curve_fit(): Unified interface for linearizable curves via variable transformation (exponential, power-law, logarithmic, hyperbolic).
    • growth_fit(): Nonlinear growth curve fitting via minpack.lm::nlsLM() with automatic starting values (Logistic, Gompertz, exponential saturation, Michaelis-Menten).
  • Refinements:
    • poly_fit() now returns explicit polynomial equations (e.g., y = 3 + 2 * x - 0.5 * x^2) instead of R formula strings.
    • Internal ._compute_fit_stats() avoids redundant AIC/BIC computation for lm models (uses stats::AIC() / stats::BIC() directly; normal-approximation fallback for nls).
    • curve_fit() documentation now notes log-transform back-transformation bias for "exp" and "power" types.
  • Dependencies (new): minpack.lm for Levenberg-Marquardt nonlinear least squares.

mathmodels 0.0.10

Regression prediction toolkit (new)

  • Model fitting (pred_reg.R):
    • reg_lm(): OLS multiple linear regression with optional stepwise selection (forward / backward / both), returning coefficients with CIs and fit statistics.
    • reg_logistic(): Binary logistic regression (glm(binomial)) with stepwise selection, odds ratios, and Hosmer-Lemeshow goodness-of-fit test.
    • reg_poisson(): Poisson regression for count data, with dispersion check.
    • reg_negbin(): Negative binomial regression via MASS::glm.nb() for over-dispersed counts.
  • Diagnostics and prediction (pred_reg.R):
    • reg_diagnostics(): Unified diagnostics — Breusch-Pagan, Durbin-Watson, Shapiro-Wilk (LM); Hosmer-Lemeshow, dispersion/deviance (GLM).
    • reg_predict(): Draw new predictor data from the empirical distribution and generate fitted or predicted values with 95% confidence intervals.
  • Visualization (pred_reg.R):
    • plot_reg_residuals(): Residual diagnostic plots (residuals vs. fitted, normal Q-Q, density).
    • plot_reg_predict(): Prediction plots with confidence bands — supports both fitted (training data) and predicted (new data) modes.

mathmodels 0.0.9

Time series toolkit (new)

Epidemic visualization & metrics overhaul

  • Renamed epidemic_plot.R → epi_plots.R. Consolidated from 3 files into 1.
  • Removed compute_incidence(), plot_infectious_curve(), plot_cumulative_infection().
  • Merged epidemic_metrics() → epi_metrics(): simplified to 4 core scalars (R0, peak_infection, peak_time, attack_rate).
  • Refined plot_incidence(): now shows ΔI (daily new infections) with peak time annotation.

Testing

  • New: test-ts.R (52 tests), test-epi_plots.R (19 tests).
  • Full suite: 539 tests, 0 failures.

mathmodels 0.0.8

New features

  • Differential equation models (diffEq_models.R):
  • Epidemic visualization (epidemic_plot.R):
    • plot_compartments(): Faceted/overlaid line plot of selected compartments.
    • compute_incidence(): Daily incidence from S or I differences.
    • plot_incidence(), plot_infectious_curve(), plot_cumulative_infection(): Infection curve visualizations.
    • plot_phase_si(): S–I phase portrait.
    • plot_Rt_estimate(): Effective reproduction number trajectory.
  • Epidemic metrics (epidemic_metrics.R):
    • epidemic_metrics(): Comprehensive epidemic summary (R0, peak, attack rate, trajectory with Rt and growth rate).
  • Markov chain prediction (markov.R):
    • markov_chain(): Transition probability matrix, multi-step prediction, stationary distribution.
    • GM11_markov(): Grey–Markov combined prediction with GM(1,1) and Markov correction.

Dependencies

  • Imports (new): Added deSolve for ODE solving.

Testing

  • 68 tests for diffEq, 23 for epidemic plots, 21 for epidemic metrics, 14 for Markov chain models. Zero errors.

mathmodels 0.0.7

New features

  • DEA models: New DEA.R module implementing Data Envelopment Analysis with:
    • basic_DEA(): Radial models (CCR/BCC) with input/output orientation support, returning both Shephard distances and Farrell efficiencies, plus slacks, targets, and lambda weights.
    • super_DEA(): Super-efficiency radial models, fully compatible with CRS/VRS settings.
    • basic_SBM(): Slacks-Based Measure (non-radial) with CRS/VRS support.
    • super_SBM(): Super-efficiency SBM for distinguishing efficient DMUs.
    • malmquist(): Malmquist productivity index supporting contemporaneous, sequential, and global references with FGNZ and Ray-Desli decompositions.
  • All DEA models use lpSolveAPI as the sole LP solver, with zero external DEA package dependency.

Dependencies

  • Imports (new): Added lpSolveAPI for linear programming in DEA models.
  • Suggests (removed): Removed deaR from Suggests; all DEA functionality is now provided natively.

Parameter validation

  • Added input validation for all DEA functions, including checks for NA values, correct column types, and valid orientation/rts/type parameters.

Testing

  • Added 30 new test cases in test-DEA.R, covering:
    • Basic DEA model validation (CCR, BCC, SBM)
    • Super-efficiency models
    • Malmquist index with all 6 type1 × type2 combinations
    • Undesirable outputs handling
    • Input validation (NA detection, minimum column requirements)
  • All 208 tests pass with zero errors.

Documentation

  • Added @examples sections to all five DEA functions with self-contained inline data.
  • Regenerated man/DEA.Rd with devtools::document().

mathmodels 0.0.6

Bug fixes

  • AHP(): Extended the Random Index (RI) table from n = 11 to n = 15, and added validation for n > 15. Previously, AHP() would silently return NA for consistency ratios with more than 11 criteria.
  • grey_corr(): Fixed incorrect default weight direction in the internal validation; weights are now correctly validated against nrow(cmp) (number of evaluation objects) rather than ncol(cmp) (number of indicators).
  • DGM21(): Fixed the list(pred = fitted, ...) return value where fitted was undefined; corrected to list(fitted = pred, ...). Also fixed the initialization value x0 from a hard-coded constant to the first element of the input series X[1].
  • grey_corr_topsis(): Fixed an internal transpose error in the call to grey_corr() that caused dimension mismatch in non-square matrices.
  • z_mf(), pi_mf(), s_mf(): Rewrote the two-stage spline algorithm for all three membership functions. The original implementation used a single-stage approximation with logically unreachable second-phase conditions, causing values to fall outside the valid [0, 1] range for certain parameter configurations. The fix ensures proper two-stage spline behavior with exact midpoint values of 0.5.

Dependencies

  • Imports (new): Added MASS, purrr, readxl, rlang, stats, stringr, and tibble to DESCRIPTION/Imports. These were already used in code via @importFrom in NAMESPACE but were missing from the formal dependency declaration.
  • Suggests (new): Added deaR to DESCRIPTION/Suggests for DEA model functionality.

Parameter validation

  • Added input type and dimension validation (stopifnot() checks) for all exported functions, including checks for:
    • Data frame / matrix type verification
    • Vector length consistency
    • Non-negativity / positivity of weights
    • Matrix dimension bounds
    • Parameter range constraints

Testing

  • Added 178 test cases across 18 test files, achieving full coverage of all core modules:
Module Test file Tests
AHP test-AHP.R 8
Combine predictions test-combine_preds.R 5
Combine weights test-combine_weights.R 7
CRITIC weighting test-critic.R 5
CV weighting test-cv.R 4
Entropy weighting test-entropy.R 7
Fuzzy evaluation test-fuzzy.R, test-fuzzy_more.R 25
Grey analysis test-grey.R 10
Grey models test-grey_models.R 16
Inequality measures test-inequality.R 12
Linear sum test-linear_sum.R 4
Membership functions test-membership.R 19
PCA weighting test-pca.R 4
Preprocessing test-preprocess.R 21
Rank sum ratio test-rsr.R 5
System evaluation test-system_evaluation.R 9
TOPSIS test-topsis.R 5

Documentation

  • Updated function examples and documentation to reflect all parameter validation rules.
  • Added pkgdown website configuration (_pkgdown.yml). Once pushed to GitHub, the reference site will be automatically built and deployed to https://zhjx19.github.io/mathmodels/ via GitHub Actions.
  • Fixed a typo in the GitHub installation URL within README.md.
  • Updated README.md version badge from 0.0.5 to 0.0.6.
  • The comprehensive online manual is available at: https://zhjx19.github.io/mathmodels-book/

mathmodels 0.0.0.9000

  • Initial development version.