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Changelog

Source: NEWS.md

bgms 0.1.6.4

New features

  • Mixed MRF models: bgm() accepts a per-variable variable_type vector that mixes "ordinal", "blume-capel", and "continuous" types to estimate networks with both discrete and continuous variables. simulate.bgms() and predict.bgms() also support mixed models.
  • Gaussian graphical models (GGM): bgm(x, variable_type = "continuous") fits a GGM with Bayesian edge selection. Pairwise effects are partial correlations from the precision matrix.
  • Missing data imputation: na_action = "impute" integrates over missing values during MCMC sampling for both ordinal and continuous models.

Bug fixes

  • fixed Blume-Capel imputation: zero-category probability had wrong sign and was double-counted. Replaced with unified loop over all categories, matching simulate_mrf().
  • fixed stale gradient cache after missing data imputation: impute_missing() updated sufficient statistics (counts_per_category_, blume_capel_stats_, pairwise_stats_) but did not invalidate the gradient cache, causing NUTS to use outdated cached values for the leapfrog integration.
  • fixed stale observation transpose after missing data imputation: the precomputed observations_double_t_ matrix was set once in the constructor but never refreshed after impute_missing() changed cells in observations_double_, causing the pairwise gradient (observations_double_t_ * E) to use stale data.

bgms 0.1.6.3

CRAN release: 2026-02-14

New features

  • extract_rhat(): extract R-hat convergence diagnostics from fitted objects
  • extract_ess(): extract effective sample size estimates from fitted objects
  • verbose argument: control informational messages; set options(bgms.verbose = FALSE) to suppress globally
  • simulate_mrf(): standalone MRF simulation with user-specified parameters
  • simulate.bgms(): generate observations from fitted models (supports parallel processing)
  • predict.bgms(): compute conditional probabilities P(X_j | X_{-j})
  • main_difference_selection argument in bgmCompare(): control threshold difference selection
  • standardize argument: scale Cauchy prior by response score range
  • baseline_category now stored in fitted object for Blume-Capel simulation/prediction

Bug fixes

  • fixed matrix indexing for posterior_mean_indicator: now correctly maps C++ row-major order to R matrices (#77)
  • fixed mass matrix adaptation: now correctly uses variance instead of precision
  • fixed step size heuristic: re-runs after mass matrix updates, resamples momentum each iteration
  • fixed E-BFMI diagnostic: now uses actual accepted trajectory momentum
  • fixed Blume-Capel interaction: uses centered scores (c - ref) in pseudolikelihood denominator

Other changes

  • NUTS: implemented generalized U-turn criterion following Betancourt (2017) and STAN
  • NUTS: fused log-posterior and gradient computation eliminates redundant probability evaluations
  • bgmCompare: BLAS-vectorized gradient computation for improved performance
  • expanded test suite: input validation, extractor functions, S3 methods, simulation, and numerical sanity tests
  • improved warmup schedule: fixed buffers (75/25/50) with proportional fallback for short warmup
  • edge selection warmup now within user budget: 85%/10%/5% split for stages 1-3a/3b/3c
  • streamlined user messages: concise warnings, consolidated NUTS diagnostics
  • E-BFMI threshold adjusted to 0.2 (standard)

Deprecated

bgms 0.1.6.2

CRAN release: 2026-01-20

New features

  • added option to separately specify beta priors for the within- and between-cluster probabilities for the SBM prior.

Other changes

  • reparameterized the Blume-capel model to use (score-baseline) instead of score.
  • implemented a new way to compute the denominators and probabilities. This made their computation both faster and more stable.
  • refactored c++ code for better maintainability.
  • removed the prepared_data field from bgm objects.

Bug fixes

  • fixed numerical problems with Blume-Capel variables using HMC and NUTS.
  • fixed a reporting bug where category thresholds for ordinal variables with a single category were incorrectly expanded to two parameters, resulting in spurious NA values.

bgms 0.1.6.1

CRAN release: 2025-10-04

Other changes

  • added extractor function for joint SBM output
  • cleaned up documentation, and c++ files
  • changed length of warmup phase I in warmup scheduler HMC / NUTS (15% → 7.5%)

Bug fixes

  • fixed a problem with warmup scheduling for adaptive-metropolis in bgmCompare()
  • fixed stability problems with parallel sampling for bgm()
  • fixed spurious output errors printing to console after user interrupt.

bgms 0.1.6.0

CRAN release: 2025-09-27

New features

  • 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

Other changes

  • 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

Bug fixes

  • fixed an out-of-bounds error in bgmCompare() when handling missing data
  • fixed a bug in the SBM prior computation

Deprecated

  • 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:
  • 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)

bgms 0.1.5.0 (GitHub only)

New features

  • 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.

Other changes

  • 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.

Bug fixes

  • 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.

bgms 0.1.4.2

CRAN release: 2024-12-05

Bug fixes

  • fixed a bug with adjusting the variance of the proposal distributions
  • fixed a bug with recoding data under the “collapse” condition

Other changes

  • 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

bgms 0.1.4.1

CRAN release: 2024-11-12

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

bgms 0.1.4

CRAN release: 2024-10-20

New features

  • 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().

Other changes

  • Exported extractor functions to extract results from bgm objects in a safe way.
  • Changed the maximum standard deviation of the adaptive proposal from 2 to 20.
  • Some small bug fixes.

bgms 0.1.3

CRAN release: 2024-02-25

New features

  • 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()

User level changes

  • Removed support of optimization based functions, mple(), mppe(), and bgm.em()
  • Removed support for the Unit-Information prior from bgm()
  • Removed support to do non-adaptive Metropolis from bgm()
  • Reduced file size when saving raw MCMC samples

bgms 0.1.2

CRAN release: 2023-10-13

This is a minor release that adds some bug fixes.

bgms 0.1.1

CRAN release: 2023-09-01

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

New features

  • 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.

User level changes

  • 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.