# Flexible Bayesian Model Selection and Model Averaging

## Documentation for package ‘FBMS’ version 1.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

FBMS-package | Flexible Bayesian Model Selection and Model Averaging
abalone | Physical measurements of 4177 abalones, a species of sea snail.
breastcancer | Breast Cancer Wisconsin (Diagnostic) Data Set
compute_effects | Compute effects for specified in labels covariates using a fitted model.
cos_deg | Cosine function for degrees
diagn_plot | Plot convergence of best/median/mean/other summary log posteriors in time
erf | erf function
exoplanet | Excerpt from the Open Exoplanet Catalogue data set
exp_dbl | Double exponential function
FBMS | Flexible Bayesian Model Selection and Model Averaging
fbms | Fit a BGNLM model using Genetically Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling. Or Fit a BGLM model using Modified Mode Jumping Markov Chain Monte Carlo (MCMC) sampling.
gauss | Gaussian function
gaussian.loglik | Log likelihood function for gaussian regression with a prior p(m)=r*sum(total_width).
gaussian.loglik.alpha | Log likelihood function for gaussian regression for alpha calculation This function is just the bare likelihood function Note that it only gives a proportional value and is equivalent to least squares
gelu | GELU function
gen.params.gmjmcmc | Generate a parameter list for GMJMCMC (Genetically Modified MJMCMC)
gen.params.mjmcmc | Generate a parameter list for MJMCMC (Mode Jumping MCMC)
gen.probs.gmjmcmc | Generate a probability list for GMJMCMC (Genetically Modified MJMCMC)
gen.probs.mjmcmc | Generate a probability list for MJMCMC (Mode Jumping MCMC)
get.best.model | Extract the Best Model from MJMCMC or GMJMCMC Results
get.mpm.model | Retrieve the Median Probability Model (MPM)
gmjmcmc | Main algorithm for GMJMCMC (Genetically Modified MJMCMC)
gmjmcmc.parallel | Run multiple gmjmcmc (Genetically Modified MJMCMC) runs in parallel returning a list of all results.
hs | heavy side function
linear.g.prior.loglik | Log likelihood function for linear regression using Zellners g-prior
logistic.loglik | Log likelihood function for logistic regression with a prior p(m)=sum(total_width) This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.
logistic.loglik.ala | Log likelihood function for logistic regression with an approximate Laplace approximations used This function is created as an example of how to create an estimator that is used to calculate the marginal likelihood of a model.
logistic.loglik.alpha | Log likelihood function for logistic regression for alpha calculation This function is just the bare likelihood function
log_prior | Log model prior function
marginal.probs | Function for calculating marginal inclusion probabilities of features given a list of models
merge_results | Merge a list of multiple results from many runs This function will weight the features based on the best marginal posterior in that population and merge the results together, simplifying by merging equivalent features (having high correlation).
mjmcmc | Main algorithm for MJMCMC (Genetically Modified MJMCMC)
mjmcmc.parallel | Run multiple mjmcmc runs in parallel, merging the results before returning.
model.string | Function to generate a function string for a model consisting of features
ngelu | Negative GELU function
nhs | negative heavy side function
not | not x
nrelu | negative ReLu function
p0 | p0 polynomial term
p05 | p05 polynomial term
p0p0 | p0p0 polynomial term
p0p05 | p0p05 polynomial term
p0p1 | p0p1 polynomial term
p0p2 | p0p2 polynomial term
p0p3 | p0p3 polynomial term
p0pm05 | p0pm05 polynomial term
p0pm1 | p0pm1 polynomial terms
p0pm2 | p0pm2 polynomial term
p2 | p2 polynomial term
p3 | p3 polynomial term
plot.gmjmcmc | Function to plot the results, works both for results from gmjmcmc and merged results from merge.results
plot.gmjmcmc_merged | Plot a gmjmcmc_merged run
plot.mjmcmc | Function to plot the results, works both for results from gmjmcmc and merged results from merge.results
plot.mjmcmc_parallel | Plot a mjmcmc_parallel run
pm05 | pm05 polynomial term
pm1 | pm1 polynomial term
pm2 | pm2 polynomial term
predict.bgnlm_model | Predict responses from a BGNLM model
predict.gmjmcmc | Predict using a gmjmcmc result object.
predict.gmjmcmc_merged | Predict using a merged gmjmcmc result object.
predict.gmjmcmc_parallel | Predict using a gmjmcmc result object from a parallel run.
predict.mjmcmc | Predict using a mjmcmc result object.
predict.mjmcmc_parallel | Predict using a mjmcmc result object from a parallel run.
print.feature | Print method for "feature" class
relu | ReLu function
rmclapply | rmclapply: Cross-platform mclapply/forking hack for Windows
SangerData2 | Gene expression data lymphoblastoid cell lines of all 210 unrelated HapMap individuals from four populations
set.transforms | Set the transformations option for GMJMCMC (Genetically Modified MJMCMC), this is also done when running the algorithm, but this function allows for it to be done manually.
sigmoid | Sigmoid function
sin_deg | Sine function for degrees
sqroot | Square root function
string.population | Function to get a character representation of a list of features
string.population.models | Function to get a character representation of a list of models
summary.gmjmcmc | Function to print a quick summary of the results
summary.gmjmcmc_merged | Function to print a quick summary of the results
summary.mjmcmc | Function to print a quick summary of the results
summary.mjmcmc_parallel | Function to print a quick summary of the results
to23 | To the 2.3 power function
to25 | To 2.5 power
to35 | To 3.5 power
to72 | To the 7/2 power function
troot | Cube root function