Function reference • hmclearn

Hamiltonian Monte Carlo (HMC) Functions and helpers for fitting models using the MCMC algorithm HMC. Also exported is the leapfrog function, largely for pedagogical purposes.
`hmc()`	Fit a generic model using Hamiltonian Monte Carlo (HMC)
`hmc.fit()`	Fitter function for Hamiltonian Monte Carlo (HMC)
`leapfrog()`	Leapfrog Algorithm for Hamiltonian Monte Carlo
Metropolis-Hastings (MH) Functions and helpers for fitting models using the MCMC algorithm MH. Also includes functions to facilitate the random walk Metropolis algorithm using a multivariate normal proposal.
`mh()`	Fit a generic model using Metropolis-Hastings (MH)
`mh.fit()`	Fitter function for Metropolis-Hastings (MH)
`qfun()`	Multivariate Normal Density of Theta1 \| Theta2
`qprop()`	Simulate from Multivariate Normal Density for Metropolis Algorithm
Plotting functions from the bayesplot package Use bayesplot to assess fit explore the posterior distributions of HMC and MH models. Includes functions for plotting Rhat statistics, effective sample sizes, histograms, density plots, trace plots, and autocorrelation, among other functions.
`mcmc_intervals()` `mcmc_areas()` `mcmc_hist()` `mcmc_hist_by_chain()` `mcmc_dens()` `mcmc_scatter()` `mcmc_hex()` `mcmc_pairs()` `mcmc_acf()` `mcmc_acf_bar()` `mcmc_trace()` `mcmc_rhat()` `mcmc_rhat_hist()` `mcmc_neff()` `mcmc_neff_hist()` `mcmc_neff_data()` `mcmc_violin()`	Plotting for MCMC visualization and diagnostics provided by `bayesplot` package
`plot(<hmclearn>)`	Plot Histograms of the Posterior Distribution
Summary Statistics from MCMC Simulations Functions to summarize and predict using the simulated posterior distribution
`summary(<hmclearn>)`	Summarizing HMC Model Fits
`coef(<hmclearn>)`	Extract Model Coefficients
`predict(<hmclearn>)`	Model Predictions for HMC or MH
MCMC diagnostics Functions for some standard MCMC diagnostics. Includes effective sample size, potential scale reduction factor (or Rhat), , and diagnostic plots that can be used to compare results from this package to fitting models using other techniques.
`neff()`	Effective sample size calculation
`neff(<hmclearn>)`	Effective sample size calculation
`psrf()`	Calculates Potential Scale Reduction Factor (psrf), also called the Rhat statistic, from models fit via `mh` or `hmc`
`psrf(<hmclearn>)`	Calculates Potential Scale Reduction Factor (psrf), also called the Rhat statistic, from models fit via `mh` or `hmc`
`diagplots()`	Diagnostic plots for `hmclearn`
`diagplots(<hmclearn>)`	Diagnostic plots for `hmclearn`
Sample Linear Regression Functions for Use in hmclearn Includes log posterior and gradient functions for Linear, Logistic, and Poisson regression models, as well as Mixed Effect models. Functions starting g_ are gradient functions.
`linear_posterior()` `g_linear_posterior()` `logistic_posterior()` `g_logistic_posterior()` `poisson_posterior()` `g_poisson_posterior()` `lmm_posterior()` `g_lmm_posterior()` `glmm_bin_posterior()` `g_glmm_bin_posterior()` `glmm_poisson_posterior()` `g_glmm_poisson_posterior()`	Sample log posterior and gradient functions for select generalized linear models and mixed effect models
Sample Data Included data sets to practice fitting models using HMC
`Drugs`	Student Drug Usage Dataset
`Endometrial`	Endometrial Cancer Dataset
`Gdat`	Count of Fresh Gopher Tortoise Shells

Reference

Hamiltonian Monte Carlo (HMC)

Metropolis-Hastings (MH)

Plotting functions from the bayesplot package

Summary Statistics from MCMC Simulations

MCMC diagnostics

Sample Linear Regression Functions for Use in hmclearn

Sample Data