Changes in version 0.99.4                        

  - The CITATION entry includes the DOI of the preprint.

                       Changes in version 0.99.3                        

  - V is now a required argument of mr_ccc().
  - sign_reversal() uses a scale-free cutoff for near-zero interaction
    draws: a draw is dropped when its standardised interaction
    coefficient is at most 1e-8 in absolute value.
  - Additional input-validation tests.

                       Changes in version 0.99.2                        

  - mr_ccc() now centres every column of the instrument matrices G, H
    and the covariate matrix V, as it already did for X, Z and Y.
    Neither first stage of the model has an intercept, so uncentred
    designs (for example genotype dosages coded 0/1/2) forced each
    first-stage fit through the origin and shifted the projections X*
    and Z* and the fixed prior scale. The documentation of
    mr_ccc_gibbs(), which does not centre its inputs, now states that
    all six inputs must be centred.
  - mr_ccc() stops with an explicit message when cbind(G, V) or cbind(H,
    V) is rank deficient after centring.
  - mr_ccc() warns when any Gelman-Rubin R-hat exceeds 1.01, and issues
    a message when only one chain is run or when the Monte Carlo
    standard error of the PIP cannot be estimated.
  - mr_ccc() and simulate_mrccc() restore the caller's random number
    state after running under a supplied seed. The fitted object records
    the g-prior scale used in settings$g.
  - mr_ccc_gibbs() validates its arguments, reports numerically singular
    matrices with an informative error, and can be interrupted from R.

                       Changes in version 0.99.1                        

  - The priors on the second-stage coefficients (beta_X, beta_XZ) and
    beta_Z are now scaled by a fixed diagonal computed once from the
    least-squares first stage, instead of by the current X*, Z* at every
    sweep. Because X* and Z* are functions of the first-stage
    parameters, the prior used in 0.99.0 entered their full
    conditionals, which the sampler did not account for. With the scale
    fixed the first-stage updates are exact as written and every update
    remains conjugate. The scale is returned in settings$prior_scale.

                       Changes in version 0.99.0                        

  - Initial Bioconductor submission.
  - mr_ccc() fits the MR-CCC model to one ligand-receptor-pathway
    triplet with one or more Gibbs chains, returning the posterior
    inclusion probability with its Monte Carlo standard error, effect
    estimates on the raw and standardised scales, pooled posterior
    draws, the joint log-likelihood of every retained draw, and the
    Gelman-Rubin statistic.
  - mr_ccc() also reports first-stage instrument strength: the partial F
    of each instrument block after the covariates, with a message when
    either block is weak by the conventional two-stage least squares
    threshold.
  - credible_intervals(), sign_probabilities(), sign_reversal() and
    plot_effect_curve() summarise the posterior draws.
  - bayes_fdr() and bfdr_threshold_set() provide Bayesian false
    discovery rate control across many triplets.
  - simulate_mrccc() generates data from the working model with an
    unmeasured confounder.
  - mr_ccc_gibbs() exposes the compiled RcppArmadillo sampler.