Changes in version 1.0.0.9000                      

                 Changes in version 1.0.0 (2026-10-23)                  

This major release introduces an improved sampler.

Model and defaults

  - Every participant has one random effect curve for correct responses
    and one for incorrect responses (random_effects =
    "correct-incorrect", the new default). A single curve for both is
    available with random_effects = "shared".
  - The maximum number of clusters at each block defaults to
    min(K^2, 2K) (8 for 4 categories) and can be set with z_max.
  - Spline coefficients that start a curve have the normal prior of
    article eq. (9), with mean and variance set from the data
    (hypers$mu_beta0_*, hypers$sigma2_beta0_*).
  - The smoothness of the fixed effects has a half-Cauchy prior on its
    standard deviation (scale hypers$s_sigma_*). The upper limits on the
    variance parameters are documented hyperparameters
    (hypers$sigma2_1_max, hypers$sigma2_us_max, hypers$sigma2_ua_max).

Improved sampler

  - A single implementation serves all boundary models, and the
    cluster-label and random-effect updates run in C++. Fits are
    about 12-21 times faster on the tone learning data.
  - The cluster labels are updated with an exact locally informed
    Hamming ball sampler, and the cluster-specific spline coefficients
    with proposals built from the prior of article eq. (9).
  - New joint moves improve mixing: a translation between fixed and
    random effects, a rescaling of the random effects together with
    their variances, and shifts of the offsets together with the drift
    and boundary levels.
  - Coefficients of unused clusters are drawn close to those of the
    clusters in use (hypers$pseudo_sd), so that curves move between
    clusters more easily.
  - Random effects start from each participant's moment estimates, and
    proposal scales are tuned during burn-in only, in batches of 50
    iterations.
  - Response-time distributions are evaluated on the log scale, which
    keeps them accurate for slow responses.

Other changes

  - New plot_predictive() for posterior predictive checks of the
    decisions and the response times.
  - Two worked examples are installed with the package
    (system.file("examples", package = "lddmm")): toy_example.R analyses
    the bundled data, and toy_simulation.R simulates data with known
    parameters and compares the posterior with the truth. The README and
    the vignette walk through fitting, posterior predictive checks and
    convergence diagnostics.
  - New arguments save_z (store cluster labels), save_loglik, informed,
    verbose and debug_checks; the fit also reports acceptance rates and
    the settings used.
  - LDDMM() checks its input and reports problems with the data.
  - log_likelihood_ind() returns a plain vector, and compute_WAIC() is
    computed on the log scale.
  - plot_accuracy() works without attaching dplyr.
  - The package no longer depends on gtools, LaplacesDemon, plyr,
    RColorBrewer, RcppProgress and rgen.
  - Unit tests, and validation scripts in tests/validation/ (a Geweke
    joint distribution test, invariance tests of individual updates, and
    a simulate-and-recover study).
  - The documentation states that response times are in seconds.

                 Changes in version 0.4.2 (2024-01-17)                  

Major changes

  - Fixed a bug in the version boundaries = "fixed".
  - Added the function input cluster to determine whether to use the
    clustering model or the unrestricted model.

                        Changes in version 0.4.1                        

Major changes

  - Added the option for constant and fixed boundaries over time
    (boundaries = "fixed-constant").

                 Changes in version 0.4.0 (2023-06-06)                  

Major changes

  - Now the option for constant boundaries over time (boundaries =
    "constant") varies across input tone and decision. The same is true
    for the random effects.
  - Now the option for fixed boundaries (boundaries = "fixed") varies
    across time and is fixed across input tone s. The same is true for
    the random effects.

                 Changes in version 0.3.0 (2023-03-26)                  

Major changes

  - Now the option for constant boundaries over time (boundaries =
    "constant") includes same boundaries per each input tone, and varies
    across possible decisions
  - Added a function to calculate the Watanabe-Akaike information
    criterion to compare model performance

                 Changes in version 0.2.1 (2023-01-31)                  

Minor changes

  - Updated documentation

                 Changes in version 0.2.0 (2023-01-28)                  

Major changes

  - Now the boundary parameter in the model can either be unrestricted
    (boundaries = "flexible"), constant over time (boundaries =
    "constant") or fixed at the same value across predictors (boundaries
    = "fixed")

Minor changes

  - Added the DOI for the article reference
  - Clarified that response times should be the log transformed of time
    (in milliseconds) for numerical stability

Bug fixes

                 Changes in version 0.1.0 (2021-11-08)                  

  - First version