# multinma 0.8.0

* Feature: Class effects models are now available, thanks to @sjperren (PR #37).
The new `class_effects` argument in `nma()` allows models with independent, 
exchangeable, or common class effects to be fitted. Class standard deviations
can also be shared between classes or subsets of classes, controlled by the
`class_sd` argument. These features are demonstrated in a new vignette,
analysing a network of interventions for social anxiety.
* Feature: Leverage plots can now be produced by `plot.nma_dic()`, with the 
option `type = "leverage"`.
* Feature: Networks with integration points can now be combined with 
`combine_network()`, where previously these were discarded. One potential use 
case is to specify different types of marginal distributions or correlation 
structures for different AgD studies in the network, by setting these up 
separately with `add_integration()` before combining with `combine_network()`.
* Feature: Added `as.data.frame.nma_dic()` and `as_tibble.nma_dic()` methods 
that return data frames of the pointwise contributions to the DIC, and
`as.matrix.nma_dic()` and `as.array.nma_dic()` methods that return posterior 
samples of the residual deviances as a matrix or 3D array.
* Feature: The `softmax()` and `inv_softmax()` transforms are now exported.
* Improvement: Removed suggested dependency on the logitnorm package. The 
logit Normal distribution functions are now implemented internally.
* Fix: Resolved a bug where trying to fit meta-regression models with discrete 
covariates would sometimes result in a misspecified and inestimable model, due 
to the inclusion of additional columns in the design matrix for the reference 
level of the covariates.
* Fix: IPD Poisson models were broken due to an incorrect offset for log time at 
risk (thanks to @n8thangreen for spotting this).
* Fix: In a Binomial model, studies where everyone experienced the outcome 
(`r = n` on all arms) no longer give `NaN` residual deviance.

# multinma 0.7.2

* Fix: Predictions for non-proportional hazards IPD NMA or ML-NMR survival 
models using `aux_regression = ~.trt` were incorrectly omitting the treatment 
effects on the auxiliary parameter(s) in some cases (#43).
* Fix: Calling `marginal_effects()` for survival outcomes with a single target
population previously gave an error.
* Fix: Predictions from exponential models where `aux_regression` had been
specified were giving an error (#44). `aux_regression` and `aux_by` have no 
effect for exponential models since there are no auxiliary (shape) parameters 
and are ignored, now with a warning.
* Fix: Avoid an error when trying to fit M-spline models combining IPD and AgD 
in R versions prior to 4.1.0, due to integer coercion of factors by `c()`.

# multinma 0.7.1

* Fix: Producing survival/hazard/cumulative hazard predictions for survival 
models with `predict()` outside of a `plot()` call no longer gives an error 
(#40).
* Fix: Increased StanHeaders version requirement to version 2.32.9 or later, to 
avoid CRAN sanitizer warnings (caused by stan-dev/rstan#1111).

# multinma 0.7.0

* Feature: The new `marginal_effects()` function produces marginal treatment
effects, as a wrapper around absolute predictions from `predict()`. For example, 
for an analysis with a binary outcome marginal odds ratios, risk ratios, or risk
differences may be produced. For survival outcomes, marginal effects may be 
based on the full range of predictions produced by `predict()`, such as marginal 
differences in restricted mean survival times, or time-varying marginal hazard 
ratios.
* Feature: Progress bars are now displayed when running interactively for
calculations with `predict()` or `marginal_effects()` from ML-NMR models that 
may take longer to run. These can be controlled with the new `progress` 
argument.
* Deprecation: The `trt_ref` argument to `predict()` has been renamed to 
`baseline_ref`; using `trt_ref` is now soft-deprecated. Renaming this argument 
`baseline_ref` follows the naming convention for the other arguments 
(`baseline_type`, `baseline_level`) that specify the details of a provided 
`baseline` distribution. This also makes way for the new `marginal_effects()` 
functionality.
* Fix: Fallback formatting used by print methods when the crayon package is not 
installed now works properly, rather than giving errors.
* Fix: Small bug caused `predict()` for AgD meta-regression models with new data 
and `baseline_type = "response"` to fail with an error.
* Fix: The number of studies on a contrast in a network plot `plot.nma_data()`
with `weight_edges = TRUE` was incorrect when a study had multiple arms of the 
same treatment. This now correctly counts the number of studies making a 
comparison, rather than the number of arms.

# multinma 0.6.1

* Fix: Piecewise exponential hazard models no longer give errors during set-up. 
Calculation of RW1 prior weights needed to be handled as a special case.

# multinma 0.6.0

## Feature: Survival/time-to-event models are now supported

* `set_ipd()` now has a `Surv` argument for specifying survival outcomes using 
`survival::Surv()`, and a new function `set_agd_surv()` sets up aggregate data 
in the form of event/censoring times (e.g. from digitized Kaplan-Meier curves) 
and overall covariate summaries.
* Left, right, and interval censoring as well as left truncation (delayed entry)
are all supported.
* The available likelihoods are Exponential (PH and AFT forms), Weibull (PH and 
AFT forms), Gompertz, log-Normal, log-Logistic, Gamma, Generalised Gamma, 
flexible M-splines on the baseline hazard, and piecewise exponential hazards.
* Auxiliary parameters (e.g. shapes, spline coefficients) are always stratified 
by study to respect randomisation, and may be further stratified by treatment
(e.g. to relax the proportional hazards assumption) and/or by additional factors
using the `aux_by` argument to `nma()`.
* A regression model may be defined for the auxiliary parameters using the 
`aux_regression` argument to `nma()`, allowing non-proportionality to be 
modelled by treatment and/or covariate effects on the shapes or spline 
coefficients.
* The `predict()` method produces estimates of survival probabilities, hazards, 
cumulative hazards, mean survival times, restricted mean survival times, 
quantiles of the survival time distribution, and median survival times. All of
these predictions can be plotted using the `plot()` method.
* The `geom_km()` function assists in plotting Kaplan-Meier curves from a 
network object, for example to overlay these on estimated survival curves. The 
`transform` argument can be used to produce log-log plots for assessing the 
proportional hazards assumption, along with cumulative hazards or log survival 
curves.
* A new vignette demonstrates ML-NMR survival analysis with an example of 
progression-free survival after autologous stem cell transplant for newly 
diagnosed multiple myeloma, with corresponding datasets `ndmm_ipd`, `ndmm_agd`,
and `ndmm_agd_covs`.

## Feature: Automatic checking of numerical integration for ML-NMR models

* The accuracy of numerical integration for ML-NMR models can now be checked 
automatically, and is by default. To do so, half of the chains are run with 
`n_int` and half with `n_int/2` integration points. Any Rhat or effective sample 
size warnings can then be ascribed to either: non-convergence of the MCMC 
chains, requiring increased number of iterations `iter` in `nma()`, or; 
insufficient accuracy of numerical integration, requiring increased number of 
integration points `n_int` in `add_integration()`. Descriptive warning messages 
indicate which is the case.
* This feature is controlled by a new `int_check` argument to `nma()`, which is 
enabled (`TRUE`) by default.
* Saving thinned cumulative integration points can now be disabled with 
`int_thin = 0`, and is now disabled by default. The previous default was
`int_thin = max(n_int %/% 10, 1)`.
* Because we can now check sufficient accuracy automatically, the default number 
of integration points `n_int` in `add_integration()` has been lowered to 64.
This is still a conservative choice, and will be sufficient in many cases; the 
previous default of 1000 was excessive.
* As a result, ML-NMR models are now much faster to run by default, both due to 
lower `n_int` and disabling saving cumulative integration points.

## Other updates

* Feature: `dic()` now includes an option to use the pV penalty instead of pD.
* Feature: The `baseline` and `aux` arguments to `predict()` can now be 
specified as the name of a study in the network, to use the parameter estimates 
from that study for prediction.
* Improvement: `predict()` will now produce aggregate-level predictions over a
sample of individuals in `newdata` for ML-NMR models (previously `newdata` had
to include integration points).
* Improvement: Compatibility with future rstan versions (PR #25).
* Improvement: Added a `plot.mcmc_array()` method, as a shortcut for 
`plot(summary(x), ...)`.
* Fix: In `plot.nma_data()`, using a custom `layout` that is not a string (e.g. 
a data frame of layout coordinates) now works as expected when `nudge > 0`.
* Fix: Documentation corrections (PR #24).
* Fix: Added missing `as.tibble.stan_nma()` and `as_tibble.stan_nma()` methods, 
to complement the existing `as.data.frame.stan_nma()`.
* Fix: Bug in ordered multinomial models where data in studies with missing 
categories could be assigned the wrong category (#28).

# multinma 0.5.1

* Fix: Now compatible with latest StanHeaders v2.26.25 (fixes #23)
* Fix: Dealt with various tidyverse deprecations
* Fix: Updated TSD URLs again (thanks to @ndunnewind)

# multinma 0.5.0

* Feature: Treatment labels in network plots can now be nudged away from the
nodes when `weight_nodes = TRUE`, using the new `nudge` argument to
`plot.nma_data()` (#15).
* Feature: The data frame returned by calling `as_tibble()` or `as.data.frame()`
on an `nma_summary` object (such as relative effects or predictions) now
includes columns for the corresponding treatment (`.trt`) or contrast (`.trta`
and `.trtb`), and a `.category` column may be included for multinomial models.
Previously these details were only present as part of the `parameter` column
* Feature: Added log t prior distribution `log_student_t()`, which can be used
for positive-valued parameters (e.g. heterogeneity variance).
* Improvement: `set_agd_contrast()` now produces an informative error message
when the covariance matrix implied by the `se` column is not positive definite.
Previously this was only checked by Stan after calling the `nma()` function.
* Improvement: Updated plaque psoriasis ML-NMR vignette to include new analyses,
including assessing the assumptions of population adjustment and synthesising
multinomial outcomes.
* Improvement: Improved behaviour of the `.trtclass` special in regression
formulas, now main effects of `.trtclass` are always removed since these are
collinear with `.trt`. This allows expansion of interactions with `*` to work
properly, e.g. `~variable*.trtclass`, whereas previously this resulted in an
over-parametrised model.
* Fix: CRAN check note for manual HTML5 compatibility.
* Fix: Residual deviance and log likelihood parameters are now named correctly
when only contrast-based aggregate data is present (PR #19).

# multinma 0.4.2

* Fix: Error in `get_nodesplits()` when studies have multiple arms of the same
treatment.
* Fix: `print.nma_data()` now prints the repeated arms when studies have
multiple arms of the same treatment.
* Fix: CRAN warning regarding invalid img tag height attribute in documentation.

# multinma 0.4.1

* Fix: tidyr v1.2.0 breaks ordered multinomial models when some studies do not
report all categories (i.e. some multinomial category outcomes are `NA` in
`multi()`) (PR #11)

# multinma 0.4.0

* Feature: Node-splitting models for assessing inconsistency are now available
with `consistency = "nodesplit"` in `nma()`. Comparisons to split can be chosen
using the `nodesplit` argument, by default all possibly inconsistent comparisons
are chosen using `get_nodesplits()`. Node-splitting results can be summarised
with `summary.nma_nodesplit()` and plotted with `plot.nodesplit_summary()`.
* Feature: The correlation matrix for generating integration points with
`add_integration()` for ML-NMR models is now adjusted to the underlying Gaussian
copula, so that the output correlations of the integration points better match
the requested input correlations. A new argument `cor_adjust` controls this
behaviour, with options `"spearman"`, `"pearson"`, or `"none"`. Although these
correlations typically have little impact on the results, for strict
reproducibility the old behaviour from version 0.3.0 and below is available with
`cor_adjust = "legacy"`.
* Feature: For random effects models, the predictive distribution of
relative/absolute effects in a new study can now be obtained in
`relative_effects()` and `predict.stan_nma()` respectively, using the new
argument `predictive_distribution = TRUE`.
* Feature: Added option to calculate SUCRA values when summarising the posterior
treatment ranks with `posterior_ranks()` or `posterior_rank_probs()`, when
argument `sucra = TRUE`.
* Improvement: Factor order is now respected when `trt`, `study`, or `trt_class`
are factors, previously the order of levels was reset into natural sort order.
* Improvement: Update package website to Bootstrap 5 with release of pkgdown
2.0.0
* Fix: Model fitting is now robust to non-default settings of
`options("contrasts")`.
* Fix: `plot.nma_data()` no longer gives a ggplot deprecation warning (PR #6).
* Fix: Bug in `predict.stan_nma()` with a single covariate when `newdata` is a
`data.frame` (PR #7).
* Fix: Attempting to call `predict.stan_nma()` on a regression model with only
contrast data and no `newdata` or `baseline` specified now throws a descriptive
error message.

# multinma 0.3.0

* Feature: Added `baseline_type` and `baseline_level` arguments to
`predict.stan_nma()`, which allow baseline distributions to be specified on the
response or linear predictor scale, and at the individual or aggregate level.
* Feature: The `baseline` argument to `predict.stan_nma()` can now accept a
(named) list of baseline distributions if `newdata` contains multiple studies.
* Improvement: Misspecified `newdata` arguments to functions like
`relative_effects()` and `predict.stan_nma()` now give more informative error
messages.
* Fix: Constructing models with contrast-based data previously gave errors in
some scenarios (ML-NMR models, UME models, and in some cases AgD meta-regression
models).
* Fix: Ensure CRAN additional checks with `--run-donttest` run correctly.

# multinma 0.2.1

* Fix: Producing relative effect estimates for all contrasts using
`relative_effects()` with `all_contrasts = TRUE` no longer gives an error for
regression models.
* Fix: Specifying the covariate correlation matrix `cor` in `add_integration()`
is not required when only one covariate is present.
* Improvement: Added more detailed documentation on the likelihoods and link
functions available for each data type (`likelihood` and `link` arguments in
`nma()`).

# multinma 0.2.0

* Feature: The `set_*()` functions now accept `dplyr::mutate()` style semantics,
allowing inline variable transformations.
* Feature: Added ordered multinomial models, with helper function `multi()` for
specifying the outcomes. Accompanied by a new data set `hta_psoriasis` and
vignette.
* Feature: Implicit flat priors can now be specified, on any parameter, using
`flat()`.
* Improvement: `as.array.stan_nma()` is now much more efficient, meaning that
many post-estimation functions are also now much more efficient.
* Improvement: `plot.nma_dic()` is now more efficient, particularly with large
numbers of data points.
* Improvement: The layering of points when producing "dev-dev" plots using
`plot.nma_dic()` with multiple data types has been reversed for improved clarity
(now AgD over the top of IPD).
* Improvement: Aggregate-level predictions with `predict()` from ML-NMR / IPD
regression models are now calculated in a much more memory-efficient manner.
* Improvement: Added an overview of examples given in the vignettes.
* Improvement: Network plots with `weight_edges = TRUE` no longer produce
legends with non-integer values for the number of studies.
* Fix: `plot.nma_dic()` no longer gives an error when attempting to specify
`.width` argument when producing "dev-dev" plots.

# multinma 0.1.3

* Format DESCRIPTION to CRAN requirements

# multinma 0.1.2

* Wrapped long-running examples in `\donttest{}` instead of `\dontrun{}`

# multinma 0.1.1

* Reduced size of vignettes
* Added methods paper reference to DESCRIPTION
* Added zenodo DOI

# multinma 0.1.0

* Feature: Network plots, using a `plot()` method for `nma_data` objects.
* Feature: `as.igraph()`, `as_tbl_graph()` methods for `nma_data` objects.
* Feature: Produce relative effect estimates with `relative_effects()`,
posterior ranks with `posterior_ranks()`, and posterior rank probabilities with
`posterior_rank_probs()`. These will be study-specific when a regression model
is given.
* Feature: Produce predictions of absolute effects with a `predict()` method for
`stan_nma` objects.
* Feature: Plots of relative effects, ranks, predictions, and parameter
estimates via `plot.nma_summary()`.
* Feature: Optional `sample_size` argument for `set_agd_*()` that:
  - Enables centering of predictors (`center = TRUE`) in `nma()` when
    a regression model is given, replacing the `agd_sample_size` argument of `nma()`
  - Enables production of study-specific relative effects, rank probabilities,
    etc. for studies in the network when a regression model is given
  - Allows nodes in network plots to be weighted by sample size
* Feature: Plots of residual deviance contributions for a model and "dev-dev"
plots comparing residual deviance contributions between two models, using a
`plot()` method for `nma_dic` objects produced by `dic()`.
* Feature: Complementary log-log (cloglog) link function `link = "cloglog"` for
binomial likelihoods.
* Feature: Option to specify priors for heterogeneity on the standard deviation,
variance, or precision, with argument `prior_het_type`.
* Feature: Added log-Normal prior distribution.
* Feature: Plots of prior distributions vs. posterior distributions with
`plot_prior_posterior()`.
* Feature: Pairs plot method `pairs()`.
* Feature: Added vignettes with example analyses from the NICE TSDs and more.
* Fix: Random effects models with even moderate numbers of studies could be very
slow. These now run much more quickly, using a sparse representation of the RE
correlation matrix which is automatically enabled for sparsity above 90%
(roughly equivalent to 10 or more studies).

# multinma 0.0.1

* Initial release.
