MCMCprecision 0.4.0
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* Bug fixes for issues concerning class(matrix(...)) in R 4.0.0


MCMCprecision 0.3.9
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* Updated citation and vignette: Paper in Statistics & Computing (doi:10.1007/s11222-018-9828-0)


MCMCprecision 0.3.8
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* Code refactoring
* Renamed functions: table.mc -> transitions; sim.mc -> rmarkov; dirichlet.mle -> fit_dirichlet ; stationary.mle -> stationary_mle ; best.k -> best_models
* Added unit tests
* Fixed bugs for transitions() of multiple-chain sequences and multiple CPUs in stationary()


MCMCprecision 0.3.6
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* Fixed WARNING: Found ‘__assert_fail’, possibly from ‘assert’ (C)


MCMCprecision 0.3.5
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* Registered C++ routines
* Improved Description file


MCMCprecision 0.3.3
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* Alternative method to compute eigenvectors: RcppEigen package
* Improved starting values for Dirichlet estimation algorithm
* Maximum likelihood estimation of stationary distribution: stationary.mle()
* Changed default prior to epsilon=1/M (M= number of sampled models)
* Changed default method to compute eigenvalue decomposition to RcppArmadillo (method="arma")


MCMCprecision 0.3.0
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* Improved estimation of Dirichlet parameters to get effective sample size (C++ version of fixed-point algorithm by Mink, 2000)
* New function best.k() to get summary for the k models with highest posterior model probability
* Exports function rdirichlet()
* Updated licence: GPL-3 (instead of GPL-2)


MCMCprecision 0.2.1
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* New function best.k() to assess estimation uncertainty for the k models with the highest posterior model probabilities


MCMCprecision 0.2.0
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* Implementations with RcppArmadillo::eig_gen and base::eigen