bgms 0.1.6.1

Other changes

added NUTS and HMC options for sampling bgm() and bgmCompare() models
added support for running multiple chains in parallel
added user interrupt handling for parallel sampling
added Markov chain diagnostics (effective sample size and R-hat) for sampled parameters
added summary(), print(), and coef() methods for fitted objects
MCMC sampling in bgm() and bgmCompare() is now reproducible when a seed argument is specified

improved progress bar for parallel sampling
summary() now integrates the functionality of the old summary_SBM()
removed options for modeling main differences; main differences are now always estimated or selected, equivalent to the previous main_difference_model = "collapse" setting

In bgm(), the following arguments are deprecated:
- interaction_scale → use pairwise_scale
- burnin → use warmup
- save → no longer needed (all outputs are returned by default)
- threshold_alpha, threshold_beta → use main_alpha, main_beta
In bgmCompare(), arguments related to difference models are deprecated:
- main_difference_model (removed without replacement)
- reference_category → use baseline_category
- pairwise_difference_*, main_difference_* → use unified difference_* arguments
- pairwise_beta_bernoulli_*, main_beta_bernoulli_* → use unified beta_bernoulli_* arguments
- interaction_scale → use pairwise_scale
- threshold_alpha, threshold_beta → use main_alpha, main_beta
- burnin → use warmup
- save → no longer needed
Deprecated extractor functions:
- extract_edge_indicators() → use extract_indicators()
- extract_pairwise_thresholds() → use extract_category_thresholds()
Deprecated object fields:
- $gamma (pre-0.1.4) and $indicator (0.1.4–0.1.5) → replaced by $raw_samples$indicator
- $main_effects (pre-0.1.4) and $posterior_mean_main (0.1.4–0.1.5) → replaced by $raw_samples$main (raw samples) and $posterior_summary_main (summaries)

The bgmCompare function now allows for network comparison for two or more groups.
The new summary_sbm function can be used to summarize the output from the bgm function with the “Stochastic-Block” prior.
Two new data sets are included in the package: ADHD and Boredom.

The bgm function with the “Stochastic-Block” prior can now also return the sampled allocations and block probabilities, and sample and return the number of blocks.
The underlying R and c++ functions received a massive update to improve their efficiency and maintainance.
Repository moved to the Bayesian Graphical Modelling Lab organization.
Included custom c++ implementations for exp and log on Windows.

Fixed a bug in the bgmCompare function with selecting group differences of blume-capel parameters. Parameter differences that were not selected and should be fixed to zero were still updated.
Fixed a bug in the bgmCompare function with handling the samples of blume-capel parameters. Output was not properly stored.
Fixed a bug in the bgmCompare function with handling threshold estimation when missing categories and main_model = “Free”. The sufficient statistics and number of categories were not computed correctly.
Partially fixed a bug in which the bgms package is slower on Windows than on Linux or MacOS. This is because the computation of exp and log using the gcc compiler for Windows is really slow. With a custom c++ implementation, the speed is now closer to the speed achieved on Linux and MacOS.

when selection = TRUE, the burnin phase now runs 2 * burnin iterations instead of 1 * burnin. This ensures the chain starts with well-calibrated parameter values
changed the maximum standard deviation of the adaptive proposal from 20 back to 2

This is a minor release that adds some documentation and output bug fixes.

Comparing the category threshold and pairwise interaction parameters in two independent samples with bgmCompare().
The Stochastic Block model is a new prior option for the network structure in bgm().

Added support for Bayesian estimation without edge selection to bgm().
Added support for simulating data from a (mixed) binary, ordinal, and Blume-Capel MRF to mrfSampler()
Added support for analyzing (mixed) binary, ordinal, and Blume-Capel variables to bgm()

This is a minor release that adds some bug fixes.

This is a minor release adding some new features and fixing some minor bugs.

Missing data imputation for the bgm function. See the na.action option.
Prior distributions for the network structure in the bgm function. See the edge_prior option.
Adaptive Metropolis as an alternative to the current random walk Metropolis algorithm in the bgm function. See the adaptive option.

Changed the default specification of the interaction prior from UnitInfo to Cauchy. See the interaction_prior option.
Changed the default threshold hyperparameter specification from 1.0 to 0.5. See the threshold_alpha and threshold_beta options.
Analysis output now uses the column names of the data.