# hmetad 0.2.0

## New features
  - Added `allow_negative_values` argument to `fit_metad`, which models M-ratio on the identity (rather than the logarithmic) scale to allow negative values of M-ratio
  - Added ability to simulate with negative M-ratios. **Note that the `log_M` arguments to simulation functions are renamed to reflect this**
  - Added functions for empirical quantities (i.e., `type1_probabilities`, `type2_probabilities`, `joint_probabilities`, `roc1`, `roc2`, and `mean_confidence`), allowing for easy comparison with model estimates
  - Added functions for area under type 1 and type 2 ROCs (i.e., `auroc1`, `auroc2`, `auroc1_draws`, and `auroc2_draws`)
  - Added a `by_response` argument to `roc2_draws` and related functions, allowing for type 2 ROCs collapsed across type 1 responses
  
## Minor improvements and fixes
  - Added `.stimulus`, `.response`, `.confidence`, and `.joint_response` arguments to all draws functions, allowing users to specify the names of the corresponding columns
  - Confidence levels now match across the two type 1 responses in `roc2_draws`


# hmetad 0.1.2

## Bug fixes
  - Resolved error in `posterior_predict()` and `posterior_epred()` for models with multilevel effects on a single confidence level
  - `cov_matrix()` now works for scalar inputs
  - `aggregate_metad()` now removes rows with `NA` values prior to aggregation
  
## New features
  - `metac2_parameters()` function streamlines setting priors for confidence criteria

## Minor improvements and fixes
  - `aggregate_metad()` and `fit_metad()` now perform more thorough checks on the number of confidence levels, `K`
  - `aggregate_metad()` has increased efficiency

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# hmetad 0.1.1

## Bug fixes
  - Fixed bug in `linpred_draws_metad`/`linpred_rvars_metad` where `meta_c` only used first draw

## New features
  - Added `logit` option to use Stan's `multinomial_logit_lpmf`/`categorical_logit_lpmf`

## Minor improvements and fixes
  - `aggregate_metad()` now preserves column types 
  - `aggregate_metad()` and `fit_metad()` now infer `K` using the maximum confidence level (instead of the number of unique levels)
  - `aggregate_metad()` and `fit_metad()` now have more helpful errors/messages for invalid data arguments

  - Minor updates to package documentation
  
  
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# hmetad 0.1.0

`hmetad` is now on [CRAN](https://cran.r-project.org/package=hmetad)!

# hmetad 0.0.1

