Pass several arguments to DHARMa::plotQQunif().
Add silent option in simulate.sdmTMB(). Setting it to FALSE allows monitoring simulations from larger models.
est_non_rf1 and est_non_rf2 columns when all the following conditions were true:
Fix delta-gamma binomial link printing for type = 'poisson-link' #340
Add suggestion to use an optimized BLAS library to README.
Add warning if it’s detected that there were problems reloading (e.g., with readRDS()) a fitted model. Simultaneously revert the approach to how reloaded models are reattached.
Move log_ratio_mix parameter to 2nd phase with starting value of -1 instead of 0 to improve convergence.
Fix bugs for nbinom1() and nbinom2_mix() simulation.
Allow profile argument in the control list to take a character vector of parameters. This move these parameters from the outer optimization problem to the inner problem (but omits from the from the Laplace approximation). See documentation in TMB. This can considerably speed up fitting models with many fixed effects.
Add theoretical quantile residuals for the generalized gamma distribution. Thanks to J.C. Dunic. #333
Add "poisson-link" option to delta-mixture lognormal.
Fix bug in simulation from Poisson-link delta models.
Simplify the internal treatment of extra time slices (extra_time). #329 This is much less bug prone and also fixes a recently introduced bug. #335 This can slightly affect model results compared to the previous approach if extra time was used along with smoothers since the ‘fake’ extra data previously used was included when mgcv determined knot locations for smoothers.
Overhaul residuals vignette (‘article’) https://pbs-assess.github.io/sdmTMB/articles/web_only/residual-checking.html including brief intros to randomized quantile residuals, simulation-based residuals, ‘one-sample’ residuals, and uniform vs. Gaussian residuals.
Add check if prediction coordinates appear outside of fitted coordinates. #285
Fix memory issue with Tweedie family on large datasets. #302
Add experimental option to return standard normal residuals from dharma_residuals().
Make simulate.sdmTMB() not include extra_time elements.
Improved re-initialization of saved fitted model objects in new sessions.
Fix important bug in simulate.sdmTMB() method for delta families where the positive linear predictor was only getting simulated for observations present in the fitted data.
Add new "mle-mvn" type to residuals.sdmTMB() and make it the default. This is a fast option for evaluating goodness of fit that should be better than the previous default. See the details section in ?residuals.sdmTMB for details. The previous default is now called "mvn-eb" but is not recommended.
Bring dharma_residuals() back over from sdmTMBextra to sdmTMB. Add a new option in the type argument ("mle-mvn") that should make the simulation residuals consistent with the expected distribution. See the same new documentation in ?residuals.sdmTMB. The examples in ?dharma_residuals illustrate suggested use.
Fix bug in sanity() where gradient checks were missing abs() such that large negative gradients weren’t getting caught. #324
Return offset vector in fitted object as an element. Ensure any extra time rows of data in the data element of the fitted object do not include the extra time slices.
Add experimental residuals option “mle-mvn” where a single approximate posterior sample of the random effects is drawn and these are combined with the MLE fixed effects to produce residuals. This may become the default option.
Add the generalized gamma distribution (thanks to J.T. Thorson with additional work by J.C. Dunic.) See gengamma(). This distribution is still in a testing phase and is not recommended for applied use yet. #286
Detect possible issue with factor(time) in formula if same column name is used for time and extra_time is specified. #320
Improve sanity() check output when there are NA fixed effect standard errors.
Set intern = FALSE within index bias correction, which seems to be considerably faster when testing with most models.
Fix a bug likely introduced in July 2023 that caused issues when extra_time was specified. This is an important bug and models fit with extra_time between that date (if using the GitHub version) and v0.4.2.9004 (2024-02-24) should be checked against a current version of sdmTMB (v0.4.2.9005 or greater). On CRAN, this affected v0.4.0 (2023-10-20) to v0.4.2. Details:
extra_time works by padding the data with a fake row of data for every extra time element (using the first row of data as the template). This is supposed to then be omitted from the likelihood so it has no impact on model fitting beyond spacing time-series processes appropriately and setting up internal structures for forecasting. Unfortunately, a bug was introduced that caused these fake data (1 per extra time element) to be included in the likelihood.Issue error if time column has NAs. #298 #299
Fix bug in get_cog(..., format = "wide") where the time column was hardcoded to "year" by accident.
Poisson-link delta models now use a type argument in delta_gamma() and delta_lognormal(). delta_poisson_link_gamma() and delta_poisson_link_lognormal() are deprecated. #290
Delta families can now pass links that are different from the default "logit" and "log". #290
Force rebuild of CRAN binaries to fix issue with breaking Matrix ABI change causing NaN gradient errors. #288 #287
Fix crash in if sdmTMB(..., do_index = TRUE) and extra_time supplied along with predict_args = list(newdata = ...) that lacked extra_time elements.
Allow get_index() to work with missing time elements.
Add the ability to pass a custom randomized quantile function qres_func to residuals.sdmTMB().
Add check for factor random intercept columns in newdata to avoid a crash. #278 #280
Improve warnings/errors around use of do_index = TRUE and get_index() if newdata = NULL. #276
Fix prediction with offset when newdata is NULL but offset is specified. #274
Fix prediction failure when both offset and nsim are provided and model includes extra_time. #273
Fix memory issues detected by CRAN ‘Additional issues’ clang-UBSAN, valgrind.
Fix a bug predicting on new data with a specified offset and extra_time. #270
Add warning around non-factor handling of the spatial_varying formula. #269
Add experimental set_delta_model() for plotting delta models with ggeffects::ggpredict() (GitHub version only until next CRAN version).
Move add_barrier_mesh() to sdmTMBextra to avoid final INLA dependency. https://github.com/pbs-assess/sdmTMBextra
Switch to using the new fmesher package for all mesh/SPDE calculations. INLA is no longer a dependency.
Switch to diagonal.penalty = FALSE in mgcv::smoothCon(). This changes the scale of the linear component of the smoother, but should result in the same model. https://github.com/glmmTMB/glmmTMB/issues/928#issuecomment-1642862066
Implement cross validation for delta models #239
Remove ELPD from cross validation output. Use sum_loglik instead. #235
Turn on Newton optimization by default. #182
print() now checks sanity() and issues a warning if there may be issues. #176
Poisson-link delta models and censored likelihood distributions have been made considerably more robust. #186
Standard errors are now available on SD parameters etc. in tidy() #240
Fix bug in print()/tidy() for delta-model positive model component sigma_E. A recently introduce bug was causing sigma_E for the 2nd model to be reported as the 1st model component sigma_E.
Add new anisotropy plotting function.
Add anisotropic range printing. #149 by @jdunic
Create the sdmTMBextra package to remove rstan/tmbstan helpers, which were causing memory sanitizer errors on CRAN. https://github.com/pbs-assess/sdmTMBextra
The following functions are affected:
predict.sdmTMB() now takes mcmc_samples, which is output from sdmTMBextra::extract_mcmc().simulate.sdmTMB() now takes mcmc_samples, which is output from sdmTMBextra::extract_mcmc().residuals.sdmTMB() now takes mcmc_samples, which is output sdmTMBextra::predict_mle_mcmc(). This only affects residuals(..., type = "mle-mcmc").Move dharma_residuals() to sdmTMBextra to reduce heavy dependencies.
See examples in the Bayesian and residuals vignettes or in the help files for those functions within sdmTMBextra.
Various fixes to pass CRAN checks. #158
Fix memory issue highlighted by Additional issues CRAN checks. #158
‘offset’ argument can now be a character value indicating a column name. This is the preferred way of using an offset with parallel cross validation. #165
Fix parallel cross validation when using an offset vector. #165
Add leave-future-out cross validation functionality. #156
Example data qcs_grid is no longer replicated by year to save package space. #158
Add message with tidy(fit, "ran_pars") about why SEs are NA.
Add anisotropy to print() #157
Fix predict(..., type = "response", se_fit = TRUE), which involves issuing a warning and sticking to link space. #140
Relax range parameter sanity() check from 1x to 1.5x the greatest distance in the data.
Add Pearson residuals for several families. residuals(fit, type = "pearson") Useful for checking for overdispersion with N > 1 binomial or Poisson families, among other uses. See the overdisp_fun() function at: https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-for-overdispersioncomputing-overdispersion-factor
Fix bug when using residuals() or simulate() with binomial families specified via cbind() or weights = N. The binomial sample size wasn’t being passed through typically resulting in Inf/-Inf.
Add mixture families: gamma_mix(), lognormal_mix() and associated delta/hurdle families: delta_gamma_mix(), delta_lognormal_mix(). These families feature a mixture of two distributions with different means but shared variance parameters.
Add delta_beta() family.
Tweak sanity() checking of standard error size.
Export previously experimental plot_anisotropy() function. The old function is now plot_anisotropy2().
Allow passing offset data through predict.sdmTMB() via offset argument.
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.
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
ADREPORT several parameters in natural space. https://github.com/pbs-assess/sdmTMB/discussions/113
Improve robustness of model print() to more esoteric mgcv smoothers.
Let sims_var work with multiple spatially varying slopes (zeta_s); return output in named list by coefficients. #107
Add threshold_coefs to sdmTMB_simulate().
Don’t make a fake mesh for non-spatial model (faster).
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.
poly(..., raw = FALSE) on newdata. #77Add experimental sdmTMB_stacking() for ensemble model stacking weights.
Add fake mesh if random fields are all off. #59
Make predict(..., newdata = NULL) also use last.par.best 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.
Add dharma_residuals().
Fix bug in simulate.sdmTMB() and residuals.sdmTMB() for binomial family.
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().
se_fit = TRUE for breakpoint models.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.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
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).
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.
... + (1 | g) #34epsilon_predictor argument in sdmTMB() to allow a model of the spatiotemporal variance through time.penalties argument to allow for regularization.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_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.
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 with predictions on new data in models with break points
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 NEWS.md.