Skip to contents

sdmTMB 0.1.4

sdmTMB 0.1.3

sdmTMB 0.1.2

  • Switch effects = 'ran_vals' for random intercept values from tidy.sdmTMB() to match the broom.mixed package.

  • Make tidy.sdmTMB() return a tibble if the tibble package is installed. Note this could affect old code since drop = FALSE is the default for tibbles but drop = TRUE is the default for data frames (i.e., tibbles always return a data frame when subsetted).

  • Fix longstanding issue with predicting on newdata with mgcv’s t2(). Previously this was disabled because of issues. It now works as expected.

  • Add knots argument in sdmTMB(), which is passed to mgcv. A common use would be to specify end points in a cyclical spline (e.g., s(x, bs = 'cc', k = 4), knots = list(x = c(1, 3, 5, 7))) when the data don’t extend fully to the boundaries that should match up.

sdmTMB 0.1.1

  • Preparing for release on CRAN.

  • Add time-varying AR1 option (originally was always a random walk). See time_varying_type argument in ?sdmTMB.

  • Allow prediction on newdata with missing time elements. #130

  • Add check for offset() (which does not work in sdmTMB, use the offset argument instead). #131

  • Add check for random slopes (sdmTMB currently only does random intercepts, although slopes can vary spatially). #131

sdmTMB 0.1.0


  • Add vignettes on visreg, ggeffects, and delta families (thanks J. Indivero!) #83 #87 #89 Forecasting and presence-only vignettes to be merged in soon.

  • Add support for emmeans package. See ?emmeans.sdmTMB for examples.

  • Add support for effects package. The ggeffects::ggeffect() function can be used to make fast marginal effects plots. ggeffects::ggpredict() works with a custom fork of ggeffects. A pull request will be made shortly. #101

  • Add vcov(), fixef(), df.residual(), formula(), terms(), and model.frame() methods.

  • Add support for "cloglog" link. Code adapted from glmmTMB for robust likelihood implementation.

  • For delta models, by default share the anisotropy parameters as in VAST. Separate anisotropy (old behavior) can be estimated with control = sdmTMBcontrol(map = list(ln_H_input = factor(c(1, 2, 3, 4))))

  • Add experimental do_index, predict_args, and index_args in sdmTMB(). These can be used to perform prediction and index calculation at the same time as fitting. For very large datasets or meshes this can save time compared to fitting, predicting, and index calculation in 3 separate steps since the TMB AD object doesn’t have to be rebuilt. This will somewhat slow down the initial fitting.

  • Remove max_gradient and bad_eig from get_index() output.

  • Use unique locations on prediction for huge speedups on large newdata gridded data.

  • Fix bug where in rare cases get_index() would return gibberish small values.

  • Add bayesian argument, which when TRUE adds Jacobian adjustments for non-linear transformed parameters. This should be TRUE if the model will be passed to tmbstan, but FALSE otherwise. #95

  • Add experimental and not-yet-exported sdmTMB:::plot_anisotropy2().

  • Add many anisotropy, delta model, and index calculation unit tests.


  • Enable random walk random field TMB simulation in sdmTMB_simulate().

  • Add check for irregular time with AR1 or random walk processes.

  • Fix bugs introduced by delta model code (offsets with extra_time and threshold model prediction).

  • Fix bug in sanity() message with small random field SDs.


  • Add support for ‘delta’ (or ‘hurdle’) models. See examples and documentation in ?sdmTMB. This has resulted in a substantial restructuring of the internal model code. By default both model components (e.g., binomial & Gamma) share the same formula, spatial, and spatiotemporal structure, but these can be separated by supplying argument values in lists where the first element corresponds to the first model and the second element corresponds to the second model (with some limitations as described in ?sdmTMB documentation ‘Details’).

  • Add support for multiple spatially varying coefficients (used to be limited to a single variable).

  • Add compatibility with the ‘visreg’ package for visualizing conditional effects of parameters. See ?visreg_delta for examples.

  • Add MCMC residual type to residuals.sdmTMB(). These are a ‘better’ residuals but slower to calculate. See documentation ‘Details’ in ?residuals.sdmTMB.

  • Make offset an argument in sdmTMB(). Using the reserved word offset in the formula is now deprecated.

  • Add sanity() function to perform some basic sanity checks on model fits.

  • Make an sdmTMB() model object compatible with update() method.

  • Remove several deprecated arguments.

  • Overhaul examples in ?sdmTMB.

  • Use faster “low-rank sparse hessian bias-correction” TMB bias correction.

  • Add parallel processing support. See parallel argument in sdmTMBcontrol. By default, grabs value of sdmTMB.cores option. E.g. options(sdmTMB.cores = 4). Only currently enabled on Mac/Linux. Using too many cores can be much slower than 1 core.

  • Use ‘cli’ package cli_abort()/cli_warn()/cli_inform() over stop()/warning()/message().

  • Add many unit tests.


  • A package version number that was used for internal testing in the ‘delta’ branch by several people.


  • Switch to TMBad library for ~3-fold speedup(!)


  • Fix bug in predictions with poly(..., raw = FALSE) on newdata. #77


  • Add experimental sdmTMB_stacking() for ensemble model stacking weights.

  • Add fake mesh if random fields are all off. #59

  • Make predict(..., newdata = NULL) also use instead of last.par to match newdata = df.

  • Fix bug in MVN fixed-effect prior indexing

  • sims and n_sims arguments have been deprecated and standardized to nsim to match the simulate() S3 method.

  • Bias correction on get_index() and get_cog() is now selective and is just applied to the necessary derived parameters.

  • INLA projection matrix ‘A’ is now shared across spatial and spatiotemporal fields.

  • Add add_utm_columns() to ease adding UTM columns.



  • Smoothers now appear in print() output. The format should roughly match brms. The main-effect component (e.g., sdepth for s(depth)) represents the linear component and the random effect (e.g., sds(depth)) component in the output corresponds to the standard deviation of the penalized weights.

  • Add censored_poisson(link = 'log') family; implemented by @joenomiddlename

  • fields in sdmTMB() is now deprecated and replaced by spatiotemporal.

  • include_spatial in sdmTMB() is now deprecated and replaced by spatial.

  • spatial_only in sdmTMB() is now deprecated and replaced by spatiotemporal. E.g. spatial_only = TRUE is now spatiotemporal = 'off' or leaving time = NULL.

  • spde in sdmTMB() is now deprecated and replaced by mesh.

  • sdmTMB_simulate() is new and will likely replace sdmTMB_sim() eventually. sdmTMB_simulate() is set up to take a formula and a data frame and is easier to use if you want different spatial observations (and covariates) for each time slice. It can also take a fitted model and modify parts of it to simulate. Finally, this function uses TMB for simulation and so is much faster and more flexible in what it can simulate (e.g., anisotropy) than the previous version.

  • spatial_trend is now spatial_varying and accepts a one-sided formula with a single predictor of any coefficient that should varying in space as a random field. Note that you may want to include a fixed effect for the same variable to improve interpretability. If the (scaled) time column is used, it will represent a local-time-trend model as before.

  • The Tweedie power (p) parameter is now in print() and tidy() output.

  • thetaf is now tweedie_p in sdmTMB_sim().


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


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


  • 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.



  • Fix minor error in PC Matern prior


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

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

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


  • 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.


  • 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).


  • Added vignette on making pretty maps of the output


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


  • 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.


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


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


  • Add penalties argument to allow for regularization.


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


  • Fixed parameter initialization for inverse links #35

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


  • 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.


  • 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.


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


  • 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 deprecated. 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.

  • There is the start of a model description vignette. It is very much a work in progress.


  • Fixed bug in dlnorm


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


  • 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


  • 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 ee3f3ba.

  • Started keeping track of news in