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Infusion (version 2.3.0)

Inference Using Simulation

Description

Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as first described in Rousset et al. (2017) . The package implements more advanced methods described in Rousset et al. (2025) .

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Version

Install

install.packages('Infusion')

Monthly Downloads

824

Version

2.3.0

License

CeCILL-2

Maintainer

Franois Rousset

Last Published

July 22nd, 2025

Functions in Infusion (2.3.0)

get_LRboot

Summary likelihood ratio tests
Infusion-internal

Internal Infusion Functions
add_simulation

Create or augment a list of simulated distributions of summary statistics
confint.SLik

Compute confidence intervals by (profile) summary likelihood
check_raw_stats

Check linear dependencies among raw summary statistics
constr_crits

Specificying arbitrary constraints on parameters
MSL

Maximum likelihood from an inferred likelihood surface
add_reftable

Create or augment a list of simulated distributions of summary statistics
MAF.options

Control of MAF design and training
Infusion

Inference using simulation
get_nbCluster_range

Control of number of components in Gaussian mixture modelling
def_projectors

Wrapper to generate projection functions for all parameters
dMixmod

Internal S4 classes.
extractors

Summary, print and logLik methods for Infusion results.
example_reftable

Workflow for method with reference table
example_raw_proj

Workflow for primitive method, with projections
focal_refine

Refine summary likelihood profile in focal parameter values
example_raw

Workflow for primitive method, without projections
densv

Saved computations of inferred log-likelihoods
get_from

Backward-compatible extractor from summary-likelihood objects
handling_NAs

Discrete probability masses and NA/NaN/Inf in distributions of summary statistics.
multi_binning

Multivariate histogram
get_workflow_design

Workflow design
goftest

Assessing goodness of fit of inference using simulation
infer_surface

Infer a (summary) likelihood or tail probability surface from inferred likelihoods
infer_logLs

Infer log Likelihoods using simulated distributions of summary statistics
init_reftable

Define starting points in parameter space.
latent

Modeling and predicting latent variables
infer_SLik_joint

Infer a (summary) likelihood surface from a simulation table
plot.SLik

Plot fit objects
options

Infusion options settings
profile.SLik

Compute profile summary likelihood
rparam

Sample the parameter space
plot_proj

Diagnostic plots for projections
save_MAFs

Save or load MAF Python objects
predict.SLik_j

Evaluate log-likelihood for given parameters
refine

Refine estimates iteratively
plot1Dprof

Plot likelihood profiles
reparam_fit

Conversion to new parameter spaces
.update_obs

Updating an 'SLik_j' object for new data
project.character

Learn a projection method for statistics and apply it
simulate.SLik_j

Simulate method for an SLik_j object.
summLik

Model density evaluation for given data and parameters