# 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.