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Infusion News
CHANGES UP TO VERSION 2.3.12
NEW FEATURES
'fac' argument of get_workflow_design().
New function get_trypoints().
Faster evaluation of the RMSEs given by MSL(.,eval_RMSEs=TRUE) and related get_from() calls.
New function C2ST() returning a classifier test diagnosing the quality of the likelihood surface inference.
'deep_copy' argument of deforest_projectors().
'precision' argument of refine() can now also be a function, allowing more complex termination conditions to be specified.
'test_cuda' argument of config_mafR().
CHANGES UP TO VERSION 2.3.0
NEW FEATURES
New default values of 'type' and 'reset' arguments of confint.SLik_j().
'seq_nbCluster' package option replaced by 'nbClu_pow_rule_fn' option, which implements a slightly different rule, documented in help for get_nbCluster_range().
new graphic defaults.
EMCluster can be selected as an alternative to Rmixmod for the clustering step, by the 'using' argument of infer_slik_joint(), or globally by the 'mixturing' package option.
CHANGES UP TO VERSION 2.2.0
NEW FEATURES
Improved procedure for sampling new parameter points (as called by refine()).
New get_workflow_design() function to define default parameters of a simulation workflow and to control non-default values. The default design suggests a minimum simulation effort for reasonable performance.
More appropriate default and control of the number of parameter points added over iterations by a refine() call, as controlled by the new 'ntot' argument (better than by the pre-existing 'n' argument), whose default is itself controlled by get_workflow_design().
The default 'precision' threshold (retrieved by Infusion.getOption("precision")) has been increased from 0.1 to 0.25, following various simulation studies showing that this less stringent threshold yields the same coverage of the CIs as the more stringent one.
Default number of trees in random-forest projections by the 'ranger' package is set to 1000 (rather than to the default value of that package).
Masked Autoregressive Flows can be used to infer the likelihood surface, as an alternative to multivariate Gaussian mixture models. The suggested 'mafR' package is required for that purpose.
The procedure that evaluates the contents of the 'CIobject' environment in a fit object is now documented as the allCIs() function.
Modeling of latent variables is now implemented, up to computation of prediction intervals based on a predictive distribution. See help("latent").
More robust procedure for 1D profiles and confidence intervals when the likelihood surface has (even small) local maxima.
Constraints on parameters (beyond the basic box constraints) are handled: see help("constr_crits").
confint.SLik_j() can evaluate more types of intervals, as controlled by the new 'type' argument.
Bootstrap computations by SLRT() now not only include test results but also confidence intervals [computed by boot::boot.ci()] when applicable (i.e. for single-parameter test).
The 'summLik' extractor can now also compute profile likelihoods.
New 'simulate.SLik_j' method to simulate (by default) projected summary statistics, or (non-default) call the sample simulator attached to the fit object.
Extended syntax for 'projectors' argument of project.default(), allowing to copy raw summary statistics from the input to the output of this function.
Default 'seq_nbCluster' and 'maxnbCluster' package options have been modified.
n=0L can now be used in refine.SLik_j() to re-generate the projectors or the clustering without augmenting the reference table. The new reproject() and recluster() functions wrap such refine() calls, with different default arguments for CI and RMSE computations.
plot1Dprof() gets new arguments 'profiles' and 'add', and returns profile coordinates, to allow recycling of its computations and additional plot features. It also gets new arguments 'lower' and 'upper' for control of the x-range, 'cluster_args' for parallelisation, 'do_plot' to control whether a plot is actually produced from the computed coordinates
Several changes to plot2Dprof() graphic output, including a contour level for 2D confidence region, and blanking of regions with low likelihood. This function also gets new arguments 'lower' and 'upper' for control of the parameter ranges, 'profiles' [same usage as in plot1Dprof()] and 'color.palette'.
the viridisLite::turbo palette is now a default for likelihood surface plots. The 'viridis' palette used in "slice plots" is now that from grDevices::hcl.colors.
plot.SLik_j() now returns (invisibly) some graphic elements of the plot, rather than the input object.
Redefinition of plot_proj(), replacing the experimental first version of this function.
Better checks of the 'stat.obs' argument of infer_SLik_joint().
Argument 'control.Simulate' added to refine.default() and goftest(). This facilitates e.g. running different steps of an analysis on different operating systems.
projections using 'ranger' now calls it with argument 'importance' set by default to "permutation" for the initial projections and to "none" for the projections performed during refine() steps.
New functions reparam_reftable() and reparam_fit() to facilitate inferences using alternative parametrizations of the same model.
New function plot_importance() for the importance metric of a 'ranger' object.
New argument 'RMSE_n' in MSL() allows more convenient control of the RMSE computation.
New defaults and extended functionalities of refine()'s 'CIs', 'eval_RMSEs' and 'update_projectors' arguments, allowing more control over several iterations.
Default value of add_reftable(.,maxmin) modified.
The structure of 'SLik_j' objects has been modified. Element 'raw_data' has been renamed 'reftable_raw' ad a redundant copy of it elsewhere in the object has been removed.
API CHANGE
The format of the return value of profile(., return.optim=TRUE) (non-default return value) is now more formally defined, in a possibly backward-incompatible way (but this will not break any standard workflow). Further, with default return.optim=FALSE, the returned log-likelihood may additionally bear the optimization solution vector as an attribute.
DEPENDENCIES
'nloptr' is an imported package.
'spaMM' version >= 4.4.16 now required.
'geometry' is now a direct dependency (previously only indirect through 'blackbox' package).
'viridis' is replaced by 'viridisLite'.
FIXED BUGS
MSL() could fail (stop) in the case of a single inferred parameter in the workflow with projection.
SLRT(., h0 = <vector of more than one parameter> ) was broken.
Fixed documentation for the package option 'mixmodGaussianModel': the fully unconstrained covariance model has been the default since CRAN version 1.5.1.
mclust-based procedures had not been tested for a long time, hence had got one major bug and accumulated many limitations. Now all tests in the 'testthat' directory run with it.
CHANGES UP TO VERSION 2.1.0
NEW FEATURES
add_reftable() allows a more extended interface for the
Simulatefunction, which may now handle a data frame of parameter values.New default refine(., nbCluster) value, based on a more definite guess of a good value and aiming at faster analyses.
New convenient wrapper init_reftable() around the init_grid() function imported from the 'blackbox' package. Infusion now depends on versions >= 1.1.41 of that package, which include a faster init_grid() function.
plot1Dprof() handles a new plot 'type', 'type="ranges"', an improved version of 'type="zoom"'.
plot2Dprof() can use parallelisation.
plot2Dprof(., pars) syntax extended for more flexible specification of sets of profiles.
refine.default() gets new argument 'CIs' for more transparent control of CI computations.
refine.default() can now request parallel execution of ranger(), according to refine()'s 'nb_cores' argument by default, but with refine()'s 'cluster_args' argument further allowing independent control of ranger()'s 'num.threads' independently from 'nb_cores'.
project.default() gets new arguments 'use_oob' and 'is_trainset' to bypass a costly step in specific (but commonplace) cases; and also argument 'methodArgs'.
nbCluster="max" syntax allowed in some contexts for fast gaussian mixture modelling by fixing the tried number of clusters to a single value automatically generated.
The 'xLLiM' package (new 'Suggested' dependency) can be used as an alternative to 'Rmixmod' for joint density modelling, by calling infer_SLik_joint(., using="xLLiM") (purely experimental, with no convincing use yet).
New convenience function write_workflow() (removed in more recent versions).
USER-LEVEL CHANGES
The structure of 'SLik_j' objects has been modified, meaning that the result of running infer_SLik_joint() has to be re-generated to be compatible with functions of which this object is an argument.
add_reftable() and add_simulation() gain argument 'parsTable' which is an alias (and now the preferred name) for pre-existing argument 'par.grid'.
UP-TO-DATE WORKFLOW
The documentations for add_reftable() and add_simulation() have been revised, to better emphasize the inference workflow based on add_reftable() over the primitive workflow based on add_simulation(), and split in different files for clarity. The definitions of the functions have also been revised, mostly in a backward-compatible way, although subtle changes may result in the primitive workflow.
DEPENDENCY CHANGES
'matrixStats' is an imported package.
CHANGES UP TO VERSION 2.0.0
NEW FEATURES
New function SLRT() for "summary-likelihood ratio tests", including bootstrap-corrected ones. For the latter correction, another function get_LRboot() provides a fast approximation to bootstrap distribution of likelihood ratio statistic.
Better choice of initial value of optimization operations in likelihood profile computations, giving more correct 1D profile plots and likelihood ratio tests (and possibly improving all subsequent operations in iterative workflows).
More thorough implementation of parallelization in add_simulation() (and add_reftable()), allowing forking, and control of random number generator.
Finer control of parallelisation in refine(), through new features of the 'cluster_args' argument.
project() accepts (barely documented) "fastai" and "keras" methods, interfacing the packages of the same name (themselves interfacing python libraries).
New default values of various controls in calls to ranger::ranger.
infer_SLik_joint() and goftest() will check for linear dependencies among the summary statistics (which would cause bugs in goftest()), thanks to the new convenience function check_raw_stats().
MSL() now computes the hessian of summary likelihood at its maximum, to check parameter identifiability (with limited success). This hessian is included in the return value of MSL() and then in the 'MSL' environment stored in (e.g.) objects of class 'SLik_j'.
New function focal_refine() to refine the likelihood surface for given parameter values.
The previously private function predict.SLik_j() that evaluates the likelihood for specific parameter points is now part of the API.
New argument 'plot.slices' for plot.SLik_j() and plot.SLik().
New argument 'decorations' for plot1Dprof() and plot2Dprof().
Improved control of number of clusters in Gaussian mixture modelling to avoid fitting more parameters than data.
projections using 'ranger' now calls it with argument importance="permutation" which may be used to select raw statistics for the goodness of fit test.
Check by add_reftable() of its result, to detect and more clearly warn about otherwise obscure problems that may occur at later steps.
profile.SLik() gets an 'init' argument, though it's more for programming purposes than intended as a user-level feature.
New 'summLik' extractor. summLik() uses a fit object to evaluate the summary-likelihood function for distinct parameter values and even for new data.
plot_proj(), a hastily written convenience function to plot a diagnostic plot for a projection from an SLik_j object.
Several code fixes for more efficient handling of large reference tables (notably, resolving some memory issues when using "ranger" projection results).
New functions get_nbCluster_range() (and seq_nbCluster(), less directly useful) to help in controlling the number of clusters used in Gaussian mixture modelling.
get_from() gets new argument 'force' to force computation of elements that may be missing from the fit object.
USER-LEVEL CHANGES
Formals of add_reftable() modified (backward incompatibility if the former first argument 'simulations' was named in the function call).
Infusion.getOption("nb_cores") now controls ranger::predict and ranger::ranger, with the expected effect according to the general effect of 'nb_cores' in Infusion (a single CPU is used if 'nb_cores' is NULL, while ranger functions use multithreading by default, i.e., when their 'num.threads' argument is NULL).
Backward-incompatible changes in project() arguments controlling training sizes.
Backward-incompatible changes in global options controlling the number of clusters used in Gaussian mixture modelling.
FIXED BUGS
Incorrect reference to randomForest in documentation for project().
DEPENDENCY CHANGES
'caret' is back in suggested packages, but for its findLinearCombos() function rather than for any modelling method.
'ranger' is now an imported package.
CHANGES UP TO VERSION 1.5.0
NEW FEATURES
New goftest() function, which may return a test of goodness of fit.
Improved sampling of parameter space in refine().
Several functions have a new argument 'cluster_args', passed to parallel::makeCluster(), for better control of the parallel computations. These functions' 'nb_cores' argument now acts as a shortcut for cluster_args$spec, in a backward-compatible way.
add_reftable() results can be more easily subsetted thanks to a new '[' method.
For better control of 1D profiles, plot1Dprof() gets new options for argument 'type', and new argument 'control'.
Refined use of function optimizers, which generally performs better than the previously used base optimizer (optim()).
plot.SLik_j(), plot1Dprof() and plot2Dprof() now re-run MSL(., eval_RMSEs=FALSE, CIs=FALSE) when they detect a new likelihood maximum, and discard pre-existing CI/RMSE information as these depend on the maximum. plot1Dprof() and plot2Dprof() now have a return value indicating whether MSL() was re-run.
New extractor get_from() to extract elements from summary-likelihood objects in a backward-compatible way.
Better control of verbosity in refine().
Tenfold larger default 'knotNbr' for ranger() projection method than previously.
New 'logLik' extractor.
DEPENDENCY CHANGES
'Rmixmod', which had disappeared from the dependencies in recent versions, is back in Suggested packages.
'nloptr' and 'minqa' now used for optimization.
'crayon' added in Suggests, with no visible effects yet in standard use.
USER-LEVEL CHANGES
Default model for Gaussian mixture modelling changed.
add_simulation() argument 'Simulate' can now be a function (rather than a name) and the return value's attr(.,"Simulate") is always the function rather than its name.
New Infusion.options "mixturing" (which replaces some previously undocumented options).
Slightly modified parameter sampling in refine(), so that all ensuing results will be slightly modified.
FIXED BUGS
add_simulation() could fail to pass the definition of 'Simulate' to child processes in parallel computations.
project.default() could fail on one-dimensional statistics (a toy case).
CHANGES UP TO VERSION 1.4.1
NEW FEATURES
Out-of-bag projections used whenever relevant in projections using random-forests methods (which definitely improves the results over "in-bag" projections).
New 'eval_RMSEs' argument of function refine.default().
New argument 'update_projectors' in refine.default(), effective only for the reference table method.
USER-LEVEL CHANGES
'ranger' package included in Suggests; random forest by 'ranger' is the new default projection 'method' in project.character().
infer_SLik_joint() can now (automatically) provide correct results when there is *one* probability mass in the distribution of the summary statistics.
infer_SLik_joint() can now handle 'using="mclust"' (but this will still provide incorrect results when there is a probability mass in the distribution of the summary statistics).
Objects of class 'SLik_j' using projection now retain a cumulative table of unprojected simulations, from which new projections can be generated.
Revised documentation including examples for the inference method based on reference tables.
Internal methods in infer_SLik_joint() are modified by default so that numerical results are slightly modified. See its new argument 'marginalize' to control this.
Defaults Infusion options 'projTrainingSize' and 'projKnotNbr' for projection by REML have been increased, because the called procedures are more efficiently implemented in versions of spaMM >= 2.7.0.
FIXED BUGS
RMSE computation on SLik_j objects could fail following Rmixmod::mixmodCluster()'s failure to fit a bootstrap replicate.
Potential problems with figure rendering when Rstudio's graphic device was *not* used.
CHANGES UP TO VERSION 1.3.0
NEW FEATURES
New 'nb_cores', 'packages' and 'env' arguments for more widely allowing parallel simulation in add_simulation() and refine().
New '...' (or 'control.Simulate') argument in add_simulation() for controlling the 'Simulate' arguments.
Dependencies changed for parallel computations; ”doSNOW' usage has been reinstated optionally (though only 'foreach' appears in the package dependencies). See help("Infusion.options") for details.
FIXED BUGS
plot.SLik() failed for more than two parameters.
project() did not work on reference tables.
CHANGES UP TO VERSION 1.2.0
MAJOR DEPENDENCY CHANGES
DESCRIPTION now Suggests 'Rmixmod' rather than Imports it. This means that, even if 'Rmixmod' were to be archived from CRAN (which has nearly occurred in March 2018), 'Infusion' could still be installed and run without 'Rmixmod', using 'mclust' as an alternative for model-based clustering. 'Rmixmod' is still the preferred method.
'mclust', 'caret' and 'ks' have been removed from DESCRIPTION (but can still be used without changes in user's code).
Imports package 'pbapply' to draw progress bars, including in parallel computations; no longer Suggests the package 'foreach' (and optionally 'doSNOW') for the same effect.
NEW FEATURES
infer_SLik_joint() can now use procedures from the 'mclust' package (controlled by new argument 'using').
The calls to 'mclust' procedures in other Infusion functions have been revised to be comparable to calls to 'Rmixmod' procedures. This means in particular that the 'G' and 'modelNames' arguments of 'mclust' procedures are set to values equivalent to the corresponding arguments of calls to 'Rmixmod' procedures, and that Infusion uses AIC to select the number of gaussian clusters in both cases (although selection by AIC is implemented in neither of the clustering packages).
CHANGES UP TO VERSION 1.1.8
NEW FEATURES
infer_logLs() can now analyze the input distributions in parallel. This is controlled by its new 'nb_cores' argument, and may make use of another new argument, 'packages'.
CORRECTED BUGS
Infusion.options(<new values>) did not return old values.
CHANGES UP TO VERSION 1.1.0
NEW FEATURES
New function infer_SLik_joint() to infer likelihood surfaces from a simulation table where each simulated data set is drawn for a distinct (vector-valued) parameter, as is usual for reference tables in Approximate Bayesian Computation.
DEPENDENCIES
'Infusion' now depends on package 'blackbox' for several functions moved there from package 'spaMM'.