Reference

Using mcp Functions for everyday use of mcp.
mcp()	Fit Multiple Linear Segments And Their Change Points
plot(<mcpfit>)	Plot full fits
plot_pars()	Plot individual parameters
pp_check()	Posterior Predictive Checks For Mcpfit Objects
summary(<mcpfit>) fixef() ranef() print(<mcpfit>)	Summarise mcpfit objects
fitted(<mcpfit>)	Expected Values from the Posterior Predictive Distribution
predict(<mcpfit>)	Samples from the Posterior Predictive Distribution
residuals(<mcpfit>)	Compute Residuals From Mcpfit Objects
criterion() loo(<mcpfit>) waic(<mcpfit>)	Compute information criteria for model comparison
hypothesis()	Test hypotheses on mcp objects.
Axillary functions These are used internally by mcp, but are exposed here since they may be useful for other purposes. Most other useful internal functions deliver the result already in mcp(segments, sample = FALSE), so mcp() will be their API.
sd_to_prec()	Transform a prior from SD to precision.
logit()	Logit function
ilogit()	Inverse logit function
probit()	Probit function
phi()	Inverse probit function
is.mcpfit()	Checks if argument is an mcpfit object
Families Distributional families that are not available in base R.
bernoulli()	Bernoulli family for mcp
negbinomial()	Negative binomial for mcp
exponential()	Exponential family for mcp
Help and demos These datasets were simulated with mcp. There are lnks to the simulation scripts in the documentation for each of them. The simulation values will also show up if you fit a model to one of these dataset and call summary(fit). Analyses of most of these are demonstrated on the front page.
mcp_example()	Get example models and data
demo_fit	Example mcpfit for examples
Miscellaneous Stuff you would not usually consult directly.
mcpfit-class	Class mcpfit of models fitted with the mcp package
print(<mcplist>)	Print mcplist
print(<mcptext>)	Nice printing texts