R release 4.0 will drop the convention to automatically change character variables to factors, and this causes errors in internal working of several Hmsc functions. This version of Hmsc is released principally to accomodate these changes in R. Hmsc will also work in previous versions of R.
Hmsc 3.0-5 was never released to CRAN. It is a snapshot that corresponds to the on-line publication of Tikhonov et al. (2020) Joint species distribution modelling with the R-package Hmsc. Methods in Ecology and Evolution 11, 442–447. (https://doi.org/10.1111/2041-210X.13345).
Shape and rate parameters (aSigma, bSigma) for the prior Gamma distribution for the variance parameter (sigma) changed. The change will influence models with "normal" and "lognormal poisson" distributions. In particular, "lognormal poisson" will more easily tend toward zero sigma if there is no overdispersion to "poisson". However, in such cases it may be wiser to refit models with pure "poisson" distribution. You can changes these parameters with setPriors function.
Cross-validation works also when the test data set has some spatial units that were unseen in the training data.
When calling sampleMcmc with fromPrior = TRUE, the residual variance parameter sigma used Gamma rather than inverse of Gamma distribution. The same error was present when sampling the initial values for the MCMC algorithm. However, the actual MCMC algorithm (and thus the posterior distribution) was correct.
Predictions with spatial NNGP models failed if there was only one unit. Github issue #40.
Reduced-Rank Regression also works for single-species models, and more robust scaling is used for species-specific covariate matrices.
Spatial models with Gaussian Predictive Process now also works when the number of spatial locations is less than the number of sampling units.
Predictions with spatial NNGP and GPP models gave bad estimates.
Several functions failed in the development version of R (to be released as R version 4). The failures were caused by changes in R internals.
Fixed bug with delta for alignPosterior which influences sampleMcmc. See github issue #27.
plotBeta failed with argument plotTree = FALSE together with SpeciesOrder = "Tree".
Spatial models with Nearest Neighbour Gaussian Process (NNGP) failed when the number spatial locations was not equal to the number of sampling units. This could happen, for instance, if there are multiple observations on the same spatial location. The problem still persists in spatial models with Gaussian Predictive Process (GPP).
Hmsc models can be modified using update(<Hmsc model>, <new arguments>). This was achieved by adding a call component per wish in github issue #34.
evaluateModelFit can handle probit models where binary data were given as TRUE/FALSE. Earlier only numeric data (0/1) were accepted. See github issue #30.
biPlot uses equal aspect ratio in ordination biplots.