TreeBUGS 1.4.6 (January 2021)
===========

* Minor bugfix for naming of categories in plotFit() function
* Minor bugfix for BayesFactorMPT()


TreeBUGS 1.4.5 (May 2020)
===========

* added standardized regression slopes for latent-trait MPT regression with continuous predictors (i.e., traitMPT with the option predStructure)
* BayesFactorSlope() plots the posterior distribution of the unstandardized (instead of partially standardized) slope parameter
* bugfix: posterior predictive check (PPP) for MPT models with a single tree


TreeBUGS 1.4.4 (December 2019)
===========

* Improvements and bugfixes for the C++ MCMC samplers by Marius Barth.
* Bug fixes for issues concerning class(matrix(...)) in R 4.0.0


TreeBUGS 1.4.3 (March 2019)
===========

* Thinning within C++ samplers [thanks to Marius Barth]
* New default priors for alpha and beta in betaMPT: "dgamma(1,.1)T(1,)"


TreeBUGS 1.4.2 (January 2019)
===========

* License updated to GPL-3
* Standard error for WAIC
* Hexagon sticker added


TreeBUGS 1.4.1 (December 2018)
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* summarizeMCMC: Batchwise computation of summary statistics to reduce RAM overload
* WAIC implemented (based on a developmental feature of JAGS)


TreeBUGS 1.3.2 (April 2018)
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* Bugfix: extendMPT() for betaMPT
* New function to extract MCMC samples: getSamples(...)
* Possibility to extract MCMC samples for getGroupMeans(...)


TreeBUGS 1.3.1 (February 2018)
===========

* Improved BayesFactorSlope(): JZS prior (=Cauchy) or g-prior(=normal)
* Bugfixes due to uncorrelated traitMPT


TreeBUGS 1.3.0 (February 2018)
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* New option for the latent-trait MPT model: Independent Cauchy priors instead
  of a multivariate Wishart prior for the random-effects covariance matrix.


TreeBUGS 1.2.0 (January 2018)
===========

* new function BayesFactorSlope() to get Bayes factor for continuous predictors in traitMPT()
* G^2 in PPP()
* option to use mu instead of mean in genTraitMPT()
* Various bugfixes


TreeBUGS 1.1.1 (August 2017)
===========

* Various bug fixes


TreeBUGS 1.1.0 (April 2017)
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* Functions to compute Bayes factors for simple (nonhierarchical, fixed-effects) MPT models: BayesFactorMPT() and marginalMPT()
* Registration of C++ routines


TreeBUGS 1.0.3 (January 2017)
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* Bugfix in readEQN: treat numeric parameter labels as constants


TreeBUGS 1.0.3 (January 2017)
===========

* Bugfix for traitMPT in priorPredictive()
* Optional arguments (...) for plotting functions


TreeBUGS 1.0.0 (December 2016)
===========

* New function correlationPosterior() to estimate the posterior for the population correlation (taking into account the number of participants)
* Second CRAN release


TreeBUGS 0.8.2 (November 2016)
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* New function probitInverse() to get the bivariate transformation of mean and SD in probability space given a normal distribution in probit space
* Visible function summarizeMCMC() to provide TreeBUGS-specific MCMC summaries
* priorPredictive() allows to sample group-level parameters


TreeBUGS 0.8.1 (November 2016)
===========

* Zeros-trick by Smith & Batchelder (2010) for betaMPT() implemented (i.e., by using the argument alpha="zero")
* Parameter constraints result in the same parameter labels for JAGS and C++ samplers (e.g., "b=a=f" will be labeled "b")


TreeBUGS 0.8.0 (November 2016)
===========

* New function withinSubjectsEQN() that replicates an MPT model multiple times with different tree, category, and parameter labels for within-subject factorial designs
* plotFreq() uses boxplots instead of lines by default
* Posterior-predictive p-values per participant
* Bugfix: avoid negative "sigma" and reversed "rho" estimates in traitMPT() due to negative scaling parameters "xi"
* Bugfix: posteriorPredictive() always sampled data for new participant (=> false T1+T2 posterior predictive checks!)


TreeBUGS 0.7.1 (November 2016)
===========

* New function priorPredictive() to sample data sets from the prior
* New function plotPrior() to plot prior distributions for group mean, SD, and correlations
* Posterior predictive samples for new participants
* Possible to use boxplot in plotFreq()
* Improved data-generating functions to allow for parameter restrictions
* Package testing via 'testthat' added


TreeBUGS 0.7.0 (November 2016)
===========

* Possibility to define models directly in R without requiring EQN files (by providing the EQN equations in a character value)
* New function transformedParameters() to obtain posterior samples of transformed parameters for a fitted model (both on the group and individual level)
* Argument "transformedParameters" now accepts a path to a text file with transformations (one per line)
* New argument "posteriorFile" to save fitted model and posterior MCMC samples in RData-file
* Better compatibility of betaMPTcpp() results with plotting functions
* Option to plot median or mean estimates in plotParam()
* Increased stability of extendMPT()
* Fixed bug in plotFreq() if input is given by a matrix/data.frame


TreeBUGS 0.6.2 (September 2016)
===========

* Bugfix: betweenSubjects with different number of chains
* Added data "arnold2013" by Arnold, Bayen, Kuhlmann, and Vaterrodt (2013)
* More examples in R help files
* Convergence plots for simpleMPT


TreeBUGS 0.6.1 (August 2016)
===========

* First CRAN release
* Fast C++ Gibbs sampler tailored standard (simple fixed-effects) MPT models (simpleMPT)
* Adjusted summary functions to account for standard MPT models


TreeBUGS 0.6.0 (August 2016)
===========

* Fast C++ Gibbs sampler tailored to beta-MPT models (betaMPTcpp)


TreeBUGS 0.5.3 (July 2016)
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* Better weakly-informative priors for continuous predictors on the standardized scale


TreeBUGS 0.5.2 (July 2016)
===========

* New function betweenSubjectMPT() that computes between-subject comparisons (e.g., differences) of parameters from two fitted hierarchical models
* Extended plotFit() to fit observed against posterior-predicted covariances
* Hyperpriors matched by vector names
* Split vignette into "Intro" and "Extended"


TreeBUGS 0.5.1 (June 2016)
===========

* Tests for participant/person heterogeneity implemented (Smith & Batchelder, 2008)


TreeBUGS 0.5.0 (June 2016)
===========

* function fitModel() that fits both trait and beta MPT (avoids duplicated code)
* correlations of parameters are computed in R, not in JAGS anymore (increase in speed)
* argument "covStructure" removed (all correlations computed by default)
* save posterior predictive samples in traitMPT/betaMPT object (i.e., expected/predicted/observed frequencies)
* to get posterior predictive p-values, use ppp=1000 (previously: M.T1=1000)
* getParam() and getGroupMean() allow to save results in .csv-file


TreeBUGS 0.4.9 (May 2016)
===========

* T2 statistic implemented (posterior predictive check of covariance structure)
* Posterior predictive checks with parallel computation using multiple cores
* Bug fix for posterior predictive with fixed effects


TreeBUGS 0.4.8 (May 2016)
===========

* New function posteriorPredictive() to draw samples of individual frequencies from posterior
* T1 statistic now computed outside of JAGS (more stable, smaller mcmc object, additional argument M.T1 to specifiy the number of posterior samples used)


TreeBUGS 0.4.7 (April 2016)
===========

* New function plotPriorPost() to compare prior vs. posterior densities
* Default prior for traitMPT changed to xi="dunif(0,10)" for stability


TreeBUGS 0.4.6 (April 2016)
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* Parameter-specific hyperpriors for betaMPT (alpha and beta) and traitMPT (mu and xi)


TreeBUGS 0.4.5 (March 2016)
===========

* Special case of a single hierarchical parameter in traitMPT()  [Wishart reduces to chi^2]
* Allow fixed effect MPT parameters (e.g., a single guessing parameter for all participants). Specified in restrictions = list("g=FE")
* Bayesian p-value in getGroupMeans() to test whether group mean differs from overall mean
* Estimation of correlations of theta parameters in betaMPT (based on the MCMC samples; no explicit prior)


TreeBUGS 0.4.4 (March 2016)
===========

* New function extendMPT() to get additional MCMC samples for fitted traitMPT and betaMPT
* Bug fixes if parameter appear twice in model equation (e.g., u*u)


TreeBUGS 0.4.3 (February 2016)
===========

* Use own summary function (functions in runjags are slow and unstable)
* Estimation of DIC requries argument "dic=TRUE" or can be estimated afterwards by: fit$dic <- extract(fit$runjags, "dic")


TreeBUGS 0.4.2 (February 2016)
===========

* Using the package runjags instead of R2jags (better functionality, e.g., provides progress bar during parallel sampling; max.time for autojags)
* Making the function summarizeMPT() visible to allow users to recompute nice MPT summaries after changing the mcmc object (e.g., after the exclusion of MCMC samples)


TreeBUGS 0.4.1 (February 2016)
===========

* new argument "T1group" to compute T1 statistic separately for a grouping factor (e.g., experimental condition; can be one of the predictors in traitMPT)
* changed name of parameter estimate plotting function to "plotParam()"
* new generic plotting function plot() for betaMPT and traitMPT (a convenient wrapper for the convergence plots in coda)


TreeBUGS 0.4.0 (February 2016)
===========

* Possible to sample correlations AND predictors in traitMPT (using covStructure vs. predStructure)
* Defaults for traitMPT: No predictors ; correlations for all covariates that are not included in predStructure
* Changed argument name "covType" to "predType" (since it is only relevant for predStructure in traitMPT)
* New argument corProbit to specify whether to compute correlations for probability- or probit-scaled MPT parameters
* Allow to round to specific number of digits, e.g.: summary(fittedModel, round=6)
* New function getParam() to conveniently extract posterior estimates (e.g., posterior mean, median, sd)
* New function getGroupMeans() to get group estimates in traitMPT with discrete predictors (for single factors or combinations)
* Updated vignette


TreeBUGS 0.3.5 (February 2016)
===========

* Back to comma-separated data format for 'data.csv' and 'covData.csv'
* Less informative Cauchy prior as default for continuous predictors in traitMPT
* Additional argument IVprec in traitMPT to specify hyperprior for precision of continuous slope paramters
* Data-generating function genMPT using general matrices of individual parameters
* Parameter labels for output in fittedModel$mcmc$BUGSoutput


TreeBUGS 0.3.4 (February 2016)
===========

* Support for WinBUGS and OpenBUGS removed
* New function plotFreq to plot individual and mean raw frequencies per tree


TreeBUGS 0.3.3 (February 2016)
===========

* autojags fixed
* BCI and R^hat included in individual statistics
* Better checks for input arguments for betaMPT, traitMPT


TreeBUGS 0.3.2 (January 2016)
===========

* csv-format for "data" and "covData": semicolon (;) instead of comma (,) to separate cells
* Printing of summary output to "parEstFile" improved


TreeBUGS 0.3.1 (January 2016)
===========

* New function plotDistribution() to plot histograms of individual mean estimates


TreeBUGS 0.3.0 (January 2016)
===========

* Discrete factors as covariates in traitMPT with fixed and random effects (see argument: covType)
* Improved covariate handling: irrelevant columns are neglected
* Goodness of fit plots for mean frequencies (plotFit)
* Removed default values for arguments that were NULL previously
* Updated vignette
* Various Bugfixes


TreeBUGS 0.2.3 (January 2016)
===========

* Predictors can be included in traitMPT (same arguments covData and covStructure as in betaMPT)
* Nice summary for covariates in betaMPT and predictors in traitMPT
* Checked and fixed data generation and fitting for latent trait and beta MPT model
* Remaining issue: SD of parameters in betaMPT not precisely estimated
* Informative error message if N=0 in a tree for a person
* Updated vignette


TreeBUGS 0.2.2 (December 2015)
===========

* Covariates can be included in betaMPT (see arguments covData and covStructure)
* Data generation for latent trait MPT model: genTraitMPT()
* Example model files (2HTM and 2HTSM) in library path: /TreeBUGS/MPTmodels/
* Examples for readEQN and data generation (genBetaMPT, genTraitMPT)



TreeBUGS 0.2.0 (December 2015)
===========

* Implementation of Beta-MPT and latent-trait MPT model: betaMPT() ; traitMPT()
* Sample and summarize transformed parameters (e.g., "deltaD=d1-d2")
* Posterior predictive checks (T1 statistic for group and individual data)
* Basic summary and plotting functionality
* Functions to generate data according to the Beta-MPT: genBetaMPT()
* Package vignette with examples: vignette("TreeBUGS")
* Checking EQN file for consistency (identifiability etc.): readEQN()
