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LDDMM 1.0.0

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.

LDDMM 0.4.2

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.

LDDMM 0.4.1

Major changes

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

LDDMM 0.4.0

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.

LDDMM 0.3.0

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

LDDMM 0.2.1

Minor changes

• Updated documentation

LDDMM 0.2.0

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

LDDMM 0.1.0

• First version

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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