... + (1 | g)#34
sdmTMB()to allow a model of the spatiotemporal variance through time.
Fixed parameter initialization for inverse links #35
Switched Gamma ‘phi’ parameter to representing shape instead of CV to match glm(), glmmTMB(), etc.
get_generic()function can be used to grab things like standardized average values of the response across a grid. What used to be
log_totalin the raw TMB output is now
link_totalbut most users you shouldn’t notice any difference.
Overhauled the simulation function. The function is now called
sdmTMB_sim() and uses INLA functions instead of RandomFields functions for simulating the random fields.
The simulation function can now accommodate all families and links and takes an INLA mesh as input.
tidy() method (from broom and broom.mixed) to return a data frame of parameter estimates. The function can extract the fixed effects or the random effect parameters (variances, AR1 correlation, spatial range).
Added an argument
sdmTMB(). This introduces additional time slices that you can then predict on if you want to interpolate or forecast. Internally, it uses Eric Ward’s ‘weights hack’. This is also useful if you have data unevenly spaced in time and you want the gaps evenly spaced for a random walk or AR1 process (add any missing years to
make_spde() is now replaced with
make_spde() has been soft depreciated.
make_mesh() carries through the x and y column names to the predict function and is more in line with the tidyverse style of taking a data frame first.
make_mesh() can accept
cutoff as an argument (as in INLA), which is likely a better default way to specify the mesh since it scales across regions better and is line with the literature on INLA.
make_mesh() can use a binary search algorithm to find a cutoff that best matches a desired number of knots (thanks to Kelli Johnson for the idea).
Barrier meshes are now possible. See
add_barrier_mesh() for an example.
There is a pkgdown website now that gets auto generated with GitHub actions: https://pbs-assess.github.io/sdmTMB/index.html
There is the start of a model description vignette: https://github.com/pbs-assess/sdmTMB/blob/devel/vignettes/model-description.Rmd It is very much a work in progress.
Fixed bug with predictions on new data in models with break points
Overhauled cross validation function. The function now:
Added threshold parameters to the print method
Added forecasting example with the weights hack
Fixed bug in linear break point models
Fixed GAM predictions with all 0s in new data.
Add linear and logistic threshold models. #17
Added parsing of mgcv formulas for splines. #16
Added ability to predict with standard errors at the population level. This helps with making marginal-effect plots. #15
Added optimization options to aid convergence. Also added
run_extra_optimization() to run these on already fit models. Default is for no extra optimization.
Added binomial likelihood to cross validation. Git hash
Started keeping track of news in