Analysis of Scientific Evidence Using Bayesian and Likelihood Methods
Bayesian analysis of one sample from a Normal distribution with imprec...
Bayesian analysis of a Normal sample using a SIR prior.
Bayesian analysis of the binomial parameter for one sample.
simulates Bayesian updating of the binomial parameter .
Bayesian analysis of the means of two Normal samples using SIR priors.
Bayesian analysis of the binomial parameters for two samples.
Bayesian analysis of a 2 x 2 contingency table.
a simple example of the bias--variance trade-off.
function to plot diverse Beta distributions for use as Binomial priors
Bayesian analysis of n >= 2 Normal means with standard improper refere...
Bayesian regression model comparison with Bayes factors.
Bayesian t-test using reference priors.
Contingency Table Analysis in different ways
evidence: Functions and Data for Bayesian and Likelihood Analysis
generates the 100 * (1 - alpha)% most probable interval of a distribut...
Likelihood analysis of the binomial parameter for one sample.
Likelihood analysis of the binomial parameters for two samples.
A dot plot is produced for several related models showing for each mod...
Plots a simple strip chart of the observations with group means and gr...
Computes the posterior probability of having a certain disease from pr...
produces a Normality plot for the argument surrounded by eight other N...
computes the Negative Predictive Value.
A robust comparison of the location and the scale of the input vector.
Conversion of a frequentist p-value to the lower bound of the Bayes fa...
Conversion of a frequentist p-value to a lower bound of the posterior ...
calculates the positive predictive value (PPV) of a diagnostic test.
Universal Fisherian significance test with confidence interval.
Conversion of 2 props input to 2x2 contingency table
A support function that calculates the sum of squares of a data vector...
Plotting routine for dataframes of looic values.
Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach. The functions in the package also make extensive use of graphical displays for data exploration and model comparison.