R: Inference Using Simulation

# Inference Using Simulation

## Documentation for package ‘Infusion’ version 2.3.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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
def_projectors | Wrapper to generate projection functions for all parameters
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 fit objects
plot.SLikp | Plot fit objects
plot.SLik_j | Plot fit 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
print | 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