Regression with Multiple Change Points

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Documentation for package ‘mcp’ version 0.2.0

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mcp-package Regression with Multiple Change Points
bernoulli Bernoulli family for mcp
criterion Compute information criteria for model comparison
ex_ar A change point in a time series
ex_binomial Two change points between three binomial segments
ex_demo Two change points between three linear segments
ex_fit Example fit of the ex_demo dataset
ex_plateaus A change point between two plateaus
ex_quadratic A change point from plateau to quadratic
ex_rel_prior Two change points between three linear segments
ex_trig A change point between two trigonometric segments
ex_variance Two change points between three heteroskedastic segments
ex_varying One change point varying by participant
fixed.effects Summarise mcpfit objects
fixef Summarise mcpfit objects
fixef.mcpfit Summarise mcpfit objects
hypothesis Test hypotheses on mcp objects.
hypothesis.mcpfit Test hypotheses on mcp objects.
ilogit Inverse logit function
logit Logit function
LOO Compute information criteria for model comparison
loo Compute information criteria for model comparison
loo.mcpfit Compute information criteria for model comparison
mcp Fit Multiple Linear Segments And Their Change Points
mcpfit Class 'mcpfit' of models fitted with the 'mcp' package
mcpfit-class Class 'mcpfit' of models fitted with the 'mcp' package
plot Plot full fits
plot.mcpfit Plot full fits
plot_pars Plot individual parameters
print Summarise mcpfit objects
print.mcpfit Summarise mcpfit objects
print.mcpprior Print mcpprior
random.effects Summarise mcpfit objects
ranef Summarise mcpfit objects
ranef.mcpfit Summarise mcpfit objects
sd_to_prec Transform a prior from SD to precision.
summary Summarise mcpfit objects
summary.mcpfit Summarise mcpfit objects
WAIC Compute information criteria for model comparison
waic Compute information criteria for model comparison
waic.mcpfit Compute information criteria for model comparison