# sdmTMB 0.0.19.9003

• Fix bug affecting prediction with se_fit = TRUE for breakpoint models.

# sdmTMB 0.0.19.9002

• Simulation from the parameter covariance matrix works if random effects are turned off. #57

# sdmTMB 0.0.19.9000

• Smoothers s() are now penalized smoothers: they determine the degree of wiggliness (as in mgcv) and it is no longer necessary to choose an appropriate k value a priori. Models fit with previous versions of sdmTMB with s(x, k = ...) will not match models specified the same way in version >= 0.0.19 since the basis functions are now penalized. All the various mgcv::s() options should be supported but t2() (and ti() and te()) are not supported.

# sdmTMB 0.0.18.9001

• Add ELPD (expected log predictive density) to sdmTMB_cv() https://arxiv.org/abs/1507.04544

• Fix bug evaluating ... when sdmTMB_cv() was called within a function. #54

# sdmTMB 0.0.18.9000

• Fix minor error in PC Matern prior

# sdmTMB 0.0.17.9000

• Add random walk option: fields = "RW".

• Depreciate ar1_fields argument. See new fields argument in sdmTMB().

• Many packages moved from ‘Imports’ to ‘Suggests’

# sdmTMB 0.0.16.9000

• Lower default nlminb() eval.max and iter.max to 1000 and 2000.

• Added profile option in sdmTMBcontrol(). This can dramatically improve model fitting speed with many fixed effects. Note the result is likely to be slightly different with TRUE vs. FALSE.

• Added simulation from the MVN precision matrix to predict.sdmTMB(). See the sims argument.

• Added gather_sims() and spread_sims() to extract parameter simulations from the joint precision matrix in a format that matches the tidybayes package.

• Added get_index_sims() for a population index calculated from the MVN simulation draws.

• Added extract_mcmc() to extract MCMC samples if the model is passed to tmbstan.

• Added the ability to predict from a model fitted with tmbstan. See the tmbstan_model argument in predict.sdmTMB().

• Allowed for separate random field Matern range parameters for spatial and spatiotemporal fields. E.g. sdmTMB(shared_range = FALSE)

• Bounded the AR1 rho parameter between -0.999 and 0.999 to improve convergence; was -1 to 1. Please post an issue if this creates problems for your model.

• Added map, start, lower, and upper options to control model fitting. See sdmTMBcontrol().

• Added priors for all parameters. See ?sdmTMB::priors and the priors argument in sdmTMB(). PC priors are available for the random fields. See ?pc_matern and the details there.

• Moved many less-common arguments from sdmTMB() to sdmTMBcontrol().

• Fix bug in sdmTMB_cv() where fitting and testing data splits were reversed. I.e., the small chunk was fit; the big chunk was tested.

# sdmTMB 0.0.15.9000

• Added experimental penalized complexity (PC) prior as used in INLA. See arguments matern_prior_O and matern_prior_E.

• Added back normalize argument to sdmTMB() and default to FALSE. Setting to TRUE can dramatically speed up some model fits (~4 times for some test models).

# sdmTMB 0.0.14.9003

• Added vignette on making pretty maps of the output

# sdmTMB 0.0.14.9001

• Added some protections for possible user errors:
• AR1 with a spatial-only model
• Missing factor levels in time
• Coordinate systems that are too big

# sdmTMB 0.0.14.9000

• Add re_form_iid to predict.sdmTMB().

• Add map_rf option to sdmTMB(). This lets you map (fix at their starting values of zero) all random fields to produce a classic GLM/GLMM.

# sdmTMB 0.0.13.9000

• Add IID random intercepts interface. E.g. ... + (1 | g) #34

# sdmTMB 0.0.12.9000

• Add epsilon_predictor argument in sdmTMB() to allow a model of the spatiotemporal variance through time.

# sdmTMB 0.0.11.9000

• Add penalties argument to allow for regularization.

# sdmTMB 0.0.10.9001

• Fix Student-t degrees of freedom in the randomized quantile residuals

# sdmTMB 0.0.10.9000

• Fixed parameter initialization for inverse links #35

• Switched Gamma ‘phi’ parameter to representing shape instead of CV to match glm(), glmmTMB(), etc.

# sdmTMB 0.0.9.9000

• Switched the density/abundance index calculation to use the link function as opposed to a hardcoded log() so that the get_generic() function can be used to grab things like standardized average values of the response across a grid. What used to be log_total in the raw TMB output is now link_total but most users you shouldn’t notice any difference.

# sdmTMB 0.0.8.9000

• 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.

# sdmTMB 0.0.7.9001

• Allow specifying degrees of freedom in the Student-t family #29

# sdmTMB 0.0.7.9000

• Added a 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 extra_time to 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 extra_time).

• make_spde() is now replaced with make_mesh() and 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.

# sdmTMB 0.0.6.9009

• Fixed bug in dlnorm

# sdmTMB 0.0.6.9005

• Fixed bug in predictions with standard errors where one(?) parameter (a breakpoint parameter) would be passed in at its initial instead of MLE value.

# sdmTMB 0.0.6.9004

• Fixed bug with predictions on new data in models with break points

• Overhauled cross validation function. The function now:

• uses Eric’s weights hack so it can also be used for forecasting
• initializes subsequent folds at the MLE of the first fold for considerable speed increases
• works in parallel if a future plan initialized; see examples
• Added threshold parameters to the print method

• Added forecasting example with the weights hack

• Fixed bug in linear break point models

# sdmTMB 0.0.6.9002

• Fixed GAM predictions with all 0s in new data.

• Add linear and logistic threshold models. #17

# sdmTMB 0.0.5.9000

• 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 ee3f3ba.

• Started keeping track of news in NEWS.md`.