NEWS

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MCMCprecision 0.4.0 (2019-12-05)

• Bug fixes for issues concerning class(matrix(...)) in R 4.0.0

MCMCprecision 0.3.9 (2018-08-10)

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

MCMCprecision 0.3.8 (2018-04-08)

• 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 (2017-04-03)

• Fixed WARNING: Found ‘__assert_fail’, possibly from ‘assert’ (C)

MCMCprecision 0.3.5

• Registered C++ routines
• Improved Description file

MCMCprecision 0.3.3

• 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

• 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

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

MCMCprecision 0.2.0

• Implementations with RcppArmadillo::eig_gen and base::eigen