-
Add
return_tmb_report
tosimulate.sdmTMB()
. -
Add
newdata
argument tosimulate.sdmTMB()
. This enables simulating on a new data frame similar to how one would predict on new data. -
Add
mle_mvn_samples
argument tosimulate.sdmTMB()
. Defaults to "single". If "multiple", then a sample from the random effects is taken for each simulation iteration. -
Add
project()
experimental function. -
Add print method for
sdmTMB_cv()
output. #319 -
Add progress bar to
simulate.sdmTMB()
. #346 -
Add AUC and TSS examples to cross validation vignette. #268
-
Add
model
(linear predictor number) argument to coef() method. Also, write documentation for?coef.sdmTMB
. #351 -
Add helpful error message if some coordinates in make_mesh() are NA. #365
-
Add informative message if fitting with an offset but predicting with offset argument left at NULL on newdata. #372
-
Fix passing of
offset
argument through insdmTMB_cv()
. Before it was being omitted in the prediction (i.e., set to 0). #372 -
Fig bug in
exponentiate
argument fortidy()
. Setconf.int = TRUE
as default. #353 -
Fix bug in prediction from
delta_truncated_nbinom1()
anddelta_truncated_nbinom2()
families. The positive component needs to be transformed to represent the mean of the untruncated distribution first before multiplying by the probability of a non-zero. Thanks to @tom-peatman #350 -
Add
get_eao()
to calculate effective area occupied.
-
Pass several arguments to
DHARMa::plotQQunif()
. -
Add
silent
option insimulate.sdmTMB()
. Setting it toFALSE
allows monitoring simulations from larger models. -
Fix bug in
est_non_rf1
andest_non_rf2
columns when all the following conditions were true:- predicting on new data
- using a delta model
- including IID random intercepts or time-varying coefficients See #342. Thanks to @tom-peatman for the issue report.
-
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()
andnbinom2_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/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 includeextra_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 toresiduals.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 thetype
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 missingabs()
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 thedata
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
andextra_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 withextra_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:- The essence of the bug was that
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.
- The essence of the bug was that
-
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 indelta_gamma()
anddelta_lognormal()
.delta_poisson_link_gamma()
anddelta_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)
andextra_time
supplied along withpredict_args = list(newdata = ...)
that lackedextra_time
elements. -
Allow
get_index()
to work with missing time elements. -
Add the ability to pass a custom randomized quantile function
qres_func
toresiduals.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
andget_index()
ifnewdata = NULL
. #276 -
Fix prediction with
offset
whennewdata
isNULL
butoffset
is specified. #274 -
Fix prediction failure when both
offset
andnsim
are provided and model includesextra_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 withggeffects::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. glmmTMB/glmmTMB#928 (comment) -
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 takesmcmc_samples
, which is output fromsdmTMBextra::extract_mcmc()
.simulate.sdmTMB()
now takesmcmc_samples
, which is output fromsdmTMBextra::extract_mcmc()
.residuals.sdmTMB()
now takesmcmc_samples
, which is outputsdmTMBextra::predict_mle_mcmc()
. This only affectsresiduals(..., 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
- Fixes for resubmission to CRAN.
- Initial submission to CRAN.
-
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 theoverdisp_fun()
function at: https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-for-overdispersioncomputing-overdispersion-factor -
Fix bug when using
residuals()
orsimulate()
with binomial families specified viacbind()
orweights = 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 nowplot_anisotropy2()
. -
Allow passing offset data through
predict.sdmTMB()
viaoffset
argument.
-
Switch
effects = 'ran_vals'
for random intercept values fromtidy.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 sincedrop = FALSE
is the default for tibbles butdrop = 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 insdmTMB()
, 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 theoffset
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. #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
tosdmTMB_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()
, andmodel.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
, andindex_args
insdmTMB()
. 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
andbad_eig
fromget_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 whenTRUE
adds Jacobian adjustments for non-linear transformed parameters. This should beTRUE
if the model will be passed to tmbstan, butFALSE
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 insdmTMB()
. Using the reserved wordoffset
in the formula is now deprecated. -
Add
sanity()
function to perform some basic sanity checks on model fits. -
Make an
sdmTMB()
model object compatible withupdate()
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 insdmTMBcontrol
. By default, grabs value ofsdmTMB.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()
overstop()
/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 uselast.par.best
instead oflast.par
to matchnewdata = df
. -
Fix bug in MVN fixed-effect prior indexing
-
sims
andn_sims
arguments have been deprecated and standardized tonsim
to match thesimulate()
S3 method. -
Bias correction on
get_index()
andget_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()
andresiduals.sdmTMB()
for binomial family.
-
Smoothers now appear in
print()
output. The format should roughly match brms. The main-effect component (e.g.,sdepth
fors(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
insdmTMB()
is now deprecated and replaced byspatiotemporal
. -
include_spatial
insdmTMB()
is now deprecated and replaced byspatial
. -
spatial_only
insdmTMB()
is now deprecated and replaced byspatiotemporal
. E.g.spatial_only = TRUE
is nowspatiotemporal = 'off'
or leavingtime = NULL
. -
spde
insdmTMB()
is now deprecated and replaced bymesh
. -
sdmTMB_simulate()
is new and will likely replacesdmTMB_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 nowspatial_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()
andtidy()
output. -
thetaf
is nowtweedie_p
insdmTMB_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 appropriatek
value a priori. Models fit with previous versions of sdmTMB withs(x, k = ...)
will not match models specified the same way in version >= 0.0.19 since the basis functions are now penalized. All the variousmgcv::s()
options should be supported butt2()
(andti()
andte()
) are not supported.
-
Add ELPD (expected log predictive density) to
sdmTMB_cv()
https://arxiv.org/abs/1507.04544 -
Fix bug evaluating
...
whensdmTMB_cv()
was called within a function. #54
- Fix minor error in PC Matern prior
-
Add random walk option:
fields = "RW"
. -
Depreciate
ar1_fields
argument. See newfields
argument in `sdmTMB(). -
Many packages moved from 'Imports' to 'Suggests'
-
Lower default
nlminb()
eval.max
anditer.max
to 1000 and 2000. -
Added
profile
option insdmTMBcontrol()
. This can dramatically improve model fitting speed with many fixed effects. Note the result is likely to be slightly different withTRUE
vs.FALSE
. -
Added simulation from the MVN precision matrix to
predict.sdmTMB()
. See thesims
argument. -
Added
gather_sims()
andspread_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 inpredict.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
, andupper
options to control model fitting. SeesdmTMBcontrol()
. -
Added priors for all parameters. See
?sdmTMB::priors
and thepriors
argument insdmTMB()
. PC priors are available for the random fields. See?pc_matern
and the details there. -
Moved many less-common arguments from
sdmTMB()
tosdmTMBcontrol()
. -
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
andmatern_prior_E
. -
Added back
normalize
argument tosdmTMB()
and default toFALSE
. Setting toTRUE
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
topredict.sdmTMB()
. -
Add
map_rf
option tosdmTMB()
. 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 insdmTMB()
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 belog_total
in the raw TMB output is nowlink_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
tosdmTMB()
. 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 toextra_time
). -
make_spde()
is now replaced withmake_mesh()
andmake_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 acceptcutoff
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
NEWS.md
.