Learn R Programming

⚠️There's a newer version (2.2.0) of this package.Take me there.

Infusion (version 2.1.0)

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. A first approach was described in Rousset et al. (2017 ) but the package implements more advanced methods.

Copy Link

Version

Install

install.packages('Infusion')

Monthly Downloads

808

Version

2.1.0

License

CeCILL-2

Homepage

https://www.R-project.org

Maintainer

Franois Rousset

Last Published

May 3rd, 2023

Functions in Infusion (2.1.0)

Workflow for primitive method, without projections

Compute confidence intervals by (profile) summary likelihood

Inference using simulation

Create or augment a list of simulated distributions of summary statistics

Maximum likelihood from an inferred likelihood surface

Create or augment a list of simulated distributions of summary statistics

check_raw_stats

Check linear dependencies among raw summary statistics

Internal S4 classes.

Infusion-internal

Internal Infusion Functions

Saved computations of inferred log-likelihoods

example_raw_proj

Workflow for primitive method, with projections

Summary, print and logLik methods for Infusion results.

infer_SLik_joint

Infer a (summary) likelihood surface from a simulation table

Assessing goodness of fit of inference using simulation

get_nbCluster_range

Control of number of components in Gaussian mixture modelling

example_reftable

Workflow for method with reference table

Backward-compatible extractor from summary-likelihood objects

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

Summary likelihood ratio tests

Refine summary likelihood profile in focal parameter values

Multivariate histogram

Define starting points in parameter space.

project.character

Learn a projection method for statistics and apply it

Compute profile summary likelihood

Plot SLik or SLikp objects

Infusion options settings

Infer log Likelihoods using simulated distributions of summary statistics

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

Refine estimates iteratively.

Sample the parameter space

Evaluate log-likelihood for given parameters

Plot likelihood profiles

Workflow template

Model density evaluation for given data and parameters