Inference Using Simulation
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Documentation for package ‘Infusion’ version 2.2.0
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A B C D E F G H I L M N P R S misc
| Infusion-package | Inference using simulation |
-- A --
| add_reftable | Create or augment a list of simulated distributions of summary statistics |
| add_simulation | Create or augment a list of simulated distributions of summary statistics |
| allCIs | Compute confidence intervals by (profile) summary likelihood |
-- B --
| boundaries-attribute | Discrete probability masses and NA/NaN/Inf in distributions of summary statistics. |
-- C --
| check_raw_stats | Check linear dependencies among raw summary statistics |
| class:dMixmod | Internal S4 classes. |
| class:NULLorChar | Internal S4 classes. |
| class:NULLorNum | Internal S4 classes. |
| config_mafR | Control of MAF design and training |
| confint | Compute confidence intervals by (profile) summary likelihood |
| confint.SLik | Compute confidence intervals by (profile) summary likelihood |
| confint.SLikp | Compute confidence intervals by (profile) summary likelihood |
| confint.SLik_j | Compute confidence intervals by (profile) summary likelihood |
| constraints | Specificying arbitrary constraints on parameters |
| constr_crits | Specificying arbitrary constraints on parameters |
-- D --
| declare_latent | Modeling and predicting latent variables |
| deforest_projectors | Learn a projection method for statistics and apply it |
| densb | Saved computations of inferred log-likelihoods |
| densv | Saved computations of inferred log-likelihoods |
| dMixmod | Internal S4 classes. |
| dMixmod-class | Internal S4 classes. |
-- E --
| example_raw | Workflow for primitive method, without projections |
| example_raw_proj | Workflow for primitive method, with projections |
| example_reftable | Workflow for method with reference table |
| extractors | Summary, print and logLik methods for Infusion results. |
-- F --
| focal_refine | Refine summary likelihood profile in focal parameter values |
-- G --
| get_from | Backward-compatible extractor from summary-likelihood objects |
| get_from.default | Backward-compatible extractor from summary-likelihood objects |
| get_from.SLik | Backward-compatible extractor from summary-likelihood objects |
| get_from.SLik_j | Backward-compatible extractor from summary-likelihood objects |
| get_LRboot | Summary likelihood ratio tests |
| get_nbCluster_range | Control of number of components in Gaussian mixture modelling |
| get_projection | Learn a projection method for statistics and apply it |
| get_projector | Learn a projection method for statistics and apply it |
| get_workflow_design | Workflow design |
| goftest | Assessing goodness of fit of inference using simulation |
-- H --
| handling_NAs | Discrete probability masses and NA/NaN/Inf in distributions of summary statistics. |
-- I --
| infer_logLs | Infer log Likelihoods using simulated distributions of summary statistics |
| infer_logL_by_GLMM | Infer log Likelihoods using simulated distributions of summary statistics |
| infer_logL_by_Hlscv.diag | Infer log Likelihoods using simulated distributions of summary statistics |
| infer_logL_by_mclust | Infer log Likelihoods using simulated distributions of summary statistics |
| infer_logL_by_Rmixmod | Infer log Likelihoods using simulated distributions of summary statistics |
| infer_SLik_joint | Infer a (summary) likelihood surface from a simulation table |
| infer_surface | Infer a (summary) likelihood or tail probability surface from inferred likelihoods |
| infer_surface.logLs | Infer a (summary) likelihood or tail probability surface from inferred likelihoods |
| infer_surface.tailp | Infer a (summary) likelihood or tail probability surface from inferred likelihoods |
| infer_tailp | Infer log Likelihoods using simulated distributions of summary statistics |
| Infusion | Inference using simulation |
| Infusion.getOption | Infusion options settings |
| Infusion.options | Infusion options settings |
| init_grid | Define starting points in parameter space. |
| init_reftable | Define starting points in parameter space. |
-- L --
| latint | Modeling and predicting latent variables |
| load_MAFs | Save or load MAF Python objects |
| logLik | Summary, print and logLik methods for Infusion results. |
| logLik.SLik | Summary, print and logLik methods for Infusion results. |
| logLik.SLik_j | Summary, print and logLik methods for Infusion results. |
-- M --
| MAF.options | Control of MAF design and training |
| MSL | Maximum likelihood from an inferred likelihood surface |
| multi_binning | Multivariate histogram |
-- N --
| NA_handling | Discrete probability masses and NA/NaN/Inf in distributions of summary statistics. |
| neuralNet | Learn a projection method for statistics and apply it |
| NULLorChar | Internal S4 classes. |
| NULLorChar-class | Internal S4 classes. |
| NULLorNum | Internal S4 classes. |
| NULLorNum-class | Internal S4 classes. |
-- P --
| parallel | Infusion options settings |
| plot.dMixmod | Internal S4 classes. |
| plot.SLik | Plot SLik or SLikp objects |
| plot.SLikp | Plot SLik or SLikp objects |
| plot.SLik_j | Plot SLik or SLikp objects |
| plot1Dprof | Plot likelihood profiles |
| plot2Dprof | Plot likelihood profiles |
| plot_importance | Diagnostic plots for projections |
| plot_proj | Diagnostic plots for projections |
| pplatent | Modeling and predicting latent variables |
| predict.SLik_j | Evaluate log-likelihood for given parameters |
| Summary, print and logLik methods for Infusion results. | |
| print.goftest | Assessing goodness of fit of inference using simulation |
| print.logLs | Summary, print and logLik methods for Infusion results. |
| print.SLik | Summary, print and logLik methods for Infusion results. |
| print.SLikp | Summary, print and logLik methods for Infusion results. |
| print.SLik_j | Summary, print and logLik methods for Infusion results. |
| profile | Compute profile summary likelihood |
| profile.SLik | Compute profile summary likelihood |
| profile.SLik_j | Compute profile summary likelihood |
| project | Learn a projection method for statistics and apply it |
| project.character | Learn a projection method for statistics and apply it |
| project.default | Learn a projection method for statistics and apply it |
-- R --
| recluster | Refine estimates iteratively |
| refine | Refine estimates iteratively |
| refine.default | Refine estimates iteratively |
| refine.SLik | Refine estimates iteratively |
| refine.SLikp | Refine estimates iteratively |
| refine.SLik_j | Refine estimates iteratively |
| refine_nbCluster | Control of number of components in Gaussian mixture modelling |
| reparam_fit | Conversion to new parameter spaces |
| reparam_reftable | Conversion to new parameter spaces |
| reproject | Refine estimates iteratively |
| rparam | Sample the parameter space |
-- S --
| sample_volume | Sample the parameter space |
| saved_seed | Saved computations of inferred log-likelihoods |
| save_MAFs | Save or load MAF Python objects |
| seq_nbCluster | Control of number of components in Gaussian mixture modelling |
| simulate | Simulate method for an 'SLik_j' object. |
| simulate.SLik_j | Simulate method for an 'SLik_j' object. |
| SLRT | Summary likelihood ratio tests |
| summary | Summary, print and logLik methods for Infusion results. |
| summary.goftest | Assessing goodness of fit of inference using simulation |
| summary.logLs | Summary, print and logLik methods for Infusion results. |
| summary.SLik | Summary, print and logLik methods for Infusion results. |
| summary.SLikp | Summary, print and logLik methods for Infusion results. |
| summary.SLik_j | Summary, print and logLik methods for Infusion results. |
| summLik | Model density evaluation for given data and parameters |
| summLik.default | Model density evaluation for given data and parameters |
| summLik.SLik_j | Model density evaluation for given data and parameters |
-- misc --
| .update_obs | Updating an 'SLik_j' object for new data |