Changed Makevars per exchange with Stan developers.
bayesdfa 0.1.2
Changed find_inverted_chains() and invert_chains() to be compatible
with dplyr 0.8 release. Specifically, removed deprecated group_by_() and
summarise_() functions and changed code to remove unused factor
levels.
bayesdfa 0.1.3
Changed Makevars and Makevars.win to rely on CXX_STD = CXX14 from
CXX11. Also added vignette related to inclusion of covariates
bayesdfa 0.1.5
Added additional functionality to relax limits on AR(1) parameter
(phi), MA(1) parameter (theta), and flexibility in estimated the
standard deviation of latent trends. Also modified the data object
passed in to be either a wide matrix (as previously done) or a long
format data frame. The latter allows for multiple observations / time
step. Finally, an additional and alternative constraint was introduced
for Z, allowing elements to be modeled as a Dirchlet process, rather
than conventional DFA.
bayesdfa 0.1.6
Removed warning related to vignette and noLD test
bayesdfa 0.1.7
Added non-gaussian families (poisson, negative binomial, bernoulli,
Gamma, lognormal). Also included a function for doing cross validation
and calculating the expected log posterior density. Another new feature
included smooth models (Gaussian process, B-splines) as alternative
models for trends conventionally modeled as random walks. Added
functions dfa_trends(), dfa_loadings() and dfa_fitted() for extracting
trends, loadings, and fitted values.
bayesdfa 1.0.0
Added constraint on diagonal of Z matrix to keep parameter estimates
from ‘flipping’ within MCMC chains. Ensures convergence for problematic
cases. This was present in 0.1.1, but later removed.
bayesdfa 1.1.0
Following 1.0.0, included a new argument to fit_dfa() function
‘expansion_prior’ that allows user to toggle on / off the constraint. If
not included (default=FALSE), there is no constraint on the Z diagonal,
and post-hoc MCMC chain inverting resolves identifiability. If
‘expansion_prior’ = TRUE, then the positive constraint is applied, in
combination with the expansion prior for trends and loadings.
bayesdfa 1.2.0
Add penalized spline models, so that the ‘trend_model’ argument may
take on “rw” for conventional random walks, “bs” for B-splines, “ps” for
“P-splines”, or “gp” for Gaussian processes
bayesdfa 1.3.0
Change to new Stan syntax
bayesdfa 1.3.1
Versioning
bayesdfa 1.3.2
Add compatibility with new rstan
Changed weights argument to ‘inv_var_weights’ and
‘likelihood_weights’ for the glmmTMB/sdmTMB/brms style