Package: Infusion 2.2.0

Infusion: Inference Using Simulation

Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated. The package implements more advanced methods than the ones first described in: Rousset, Gouy, Almoyna and Courtiol (2017) <doi:10.1111/1755-0998.12627>.

Authors:François Rousset [aut, cre, cph]

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NEWS

# Install 'Infusion' in R:
install.packages('Infusion', repos = c('https://f-rousset.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • densb - Saved computations of inferred log-likelihoods
  • densv - Saved computations of inferred log-likelihoods
  • saved_seed - Saved computations of inferred log-likelihoods
  • saved_seed - Saved computations of inferred log-likelihoods

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.20 score 15 scripts 1.1k downloads 1 mentions 102 exports 36 dependencies

Last updated 2 months agofrom:f5a322da84. Checks:OK: 7. Indexed: yes.

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Doc / VignettesOKOct 27 2024
R-4.5-winOKOct 27 2024
R-4.5-linuxOKOct 27 2024
R-4.4-winOKOct 27 2024
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R-4.3-winOKOct 27 2024
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Exports:add_reftableadd_simulationallCIscalc.lrthresholdcalc.lrthreshold.defaultcalc.lrthreshold.SLikcalc.lrthreshold.SLikpcheck_raw_statsconfig_mafRconfint.SLikconfint.SLik_jconfint.SLikpdeclare_latentdeforest_projectorsfocal_refineget_fromget_from.defaultget_from.SLikget_from.SLik_jget_LRbootget_nbCluster_rangeget_projectionget_projectorget_workflow_designgoftestinfer_logL_by_GLMMinfer_logL_by_Hlscv.diaginfer_logL_by_mclustinfer_logL_by_Rmixmodinfer_logLsinfer_SLik_jointinfer_surfaceinfer_surface.logLsinfer_surface.tailpinfer_tailpInfusion.getOptionInfusion.optionsinit_gridinit_reftableisPointInCHulllatintload_MAFslogLik.SLiklogLik.SLik_jMAF.optionsmapMMMSLmulti_binningneuralNetplot_importanceplot_projplot.dMixmodplot.MixmodResultsplot.SLikplot.SLik_jplot.SLikpplot1Dprofplot2Dprofpplatentpredict.dMclustpredict.dMixmodpredict.MixmodResultspredict.SLikpredict.SLik_jpredict.SLikpprint.goftestprint.logLsprint.SLikprint.SLik_jprint.SLikpprofile.SLikprofile.SLik_jprojectproject.characterproject.defaultprojpathreclusterrefinerefine_nbClusterrefine.defaultrefine.SLikrefine.SLik_jrefine.SLikpreparam_fitreparam_reftablereprojectresetCHullrparamsample_volumesave_MAFsseq_nbClustersimulate.SLik_jSLRTstr.MAFsummary.goftestsummary.logLssummary.SLiksummary.SLik_jsummary.SLikpsummLiksummLik.defaultsummLik.SLik_j

Dependencies:abindbackportsblackboxbootcheckmateclicodetoolscrayonforeachgeometrygmpiteratorslatticelinproglpSolvemagicMASSMatrixmatrixStatsminqamvtnormnlmenloptrnumDerivpbapplyproxyrangerrcddRcppRcppEigenRcppProgressregistryROIslamspaMMviridisLite

Readme and manuals

Help Manual

Help pageTopics
Updating an 'SLik_j' object for new data.update_obs
Create or augment a list of simulated distributions of summary statisticsadd_reftable
Create or augment a list of simulated distributions of summary statisticsadd_simulation
Check linear dependencies among raw summary statisticscheck_raw_stats
Compute confidence intervals by (profile) summary likelihoodallCIs confint confint.SLik confint.SLikp confint.SLik_j
Specificying arbitrary constraints on parametersconstraints constr_crits
Modeling and predicting latent variablesdeclare_latent latint pplatent
Saved computations of inferred log-likelihoodsdensb densv saved_seed
Internal S4 classes.class:dMixmod class:NULLorChar class:NULLorNum dMixmod dMixmod-class NULLorChar NULLorChar-class NULLorNum NULLorNum-class plot.dMixmod
Workflow for primitive method, without projectionsexample_raw
Workflow for primitive method, with projectionsexample_raw_proj
Workflow for method with reference tableexample_reftable
Summary, print and logLik methods for Infusion results.extractors logLik logLik.SLik logLik.SLik_j print print.logLs print.SLik print.SLikp print.SLik_j summary summary.logLs summary.SLik summary.SLikp summary.SLik_j
Refine summary likelihood profile in focal parameter valuesfocal_refine
Backward-compatible extractor from summary-likelihood objectsget_from get_from.default get_from.SLik get_from.SLik_j
Summary likelihood ratio testsget_LRboot SLRT
Control of number of components in Gaussian mixture modellingget_nbCluster_range refine_nbCluster seq_nbCluster
Workflow designget_workflow_design
Assessing goodness of fit of inference using simulationgoftest print.goftest summary.goftest
Discrete probability masses and NA/NaN/Inf in distributions of summary statistics.boundaries-attribute handling_NAs NA_handling
Infer log Likelihoods using simulated distributions of summary statisticsinfer_logLs infer_logL_by_GLMM infer_logL_by_Hlscv.diag infer_logL_by_mclust infer_logL_by_Rmixmod infer_tailp
Infer a (summary) likelihood surface from a simulation tableinfer_SLik_joint
Infer a (summary) likelihood or tail probability surface from inferred likelihoodsinfer_surface infer_surface.logLs infer_surface.tailp
Inference using simulationInfusion-package Infusion
Define starting points in parameter space.init_grid init_reftable
Control of MAF design and trainingconfig_mafR MAF.options
Maximum likelihood from an inferred likelihood surfaceMSL
Multivariate histogrammulti_binning
Infusion options settingsInfusion.getOption Infusion.options parallel
Diagnostic plots for projectionsplot_importance plot_proj
Plot SLik or SLikp objectsplot.SLik plot.SLikp plot.SLik_j
Plot likelihood profilesplot1Dprof plot2Dprof
Evaluate log-likelihood for given parameterspredict.SLik_j
Compute profile summary likelihoodprofile profile.SLik profile.SLik_j
Learn a projection method for statistics and apply itdeforest_projectors get_projection get_projector neuralNet project project.character project.default
Refine estimates iterativelyrecluster refine refine.default refine.SLik refine.SLikp refine.SLik_j reproject
Conversion to new parameter spacesreparam_fit reparam_reftable
Sample the parameter spacerparam sample_volume
Save or load MAF Python objectsload_MAFs save_MAFs
Simulate method for an 'SLik_j' object.simulate simulate.SLik_j
Model density evaluation for given data and parameterssummLik summLik.default summLik.SLik_j