FBMS-package            Flexible Bayesian Model Selection and Model
                        Averaging
SangerData2             Gene expression data lymphoblastoid cell lines
                        of all 210 unrelated HapMap individuals from
                        four populations
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                    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
log_prior               Log model prior function
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
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
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
