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
mrfSampler() → use simulate_mrf()
bgms 0.1.6.2
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
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
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:
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)
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
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
This is a minor release that adds some documentation and output bug
fixes.
bgms 0.1.4
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
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
This is a minor release that adds some bug fixes.
bgms 0.1.1
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.
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