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stats::density() instead of
hdrcde::hdr() for HDPI estimation.| ArchaeoPhases 1.x | ArchaeoPhases 2.0 |
|---|---|
AgeDepth() |
bury() |
CreateMinMaxGroup() |
phase(),
as_phases() |
CredibleInterval(),
credible_interval() |
interval_credible() |
DatesHiatus(),
dates_hiatus() |
hiatus() |
estimate_range() |
sensitivity() |
MarginalPlot(),
marginal_plot() |
plot() |
MarginalProba() |
older() |
MarginalStatistics(),
marginal_statistics(),
multi_marginal_statistics() |
summary() |
MultiCredibleInterval(),
multi_credible_interval() |
interval_credible() |
MultiDatesPlot(),
multi_dates_plot() |
plot() |
MultiHPD(),
multi_hpd() |
interval_hdr() |
MultiMarginalPlot(),
multi_marginal_plot() |
plot() |
MultiPhasePlot() |
plot() |
MultiPhaseTimeRange() |
boundaries() |
MultiPhasesGap() |
hiatus() |
MultiPhasesTransition() |
transition() |
MultiSuccessionPlot() |
plot() |
OccurrencePlot(),
occurrence_plot() |
occurrence() +
plot() |
PhaseDurationPlot() |
duration() +
plot() |
PhasePlot() |
plot() |
PhaseStatistics() |
summary() |
PhaseTimeRange() |
boundaries() |
PhasesGap(),
phases_gap() |
hiatus() |
PhasesTransition() |
transition() |
SuccessionPlot() |
plot() |
TempoActivityPlot(),
tempo_activity_plot() |
activity() +
plot() |
TempoPlot(),
tempo_plot() |
tempo() +
plot() |
undated_sample() |
interpolate() |
allen_analyze(), allen_joint_concurrency(),
allen_observe_frequency(), allen_illustrate(),
allen_observe().reproduce()
function.read_bcal(),
read_oxcal(), read_chronomodel().
read_csv(), which
can read data from a file, connection, or the clipboard.multi_dates_plot(),
tempo_activity_plot(), tempo_plot(),
marginal_plot(), multi_marginal_plot(), and
occurrence_plot().
TempoPlot() ->
tempo_plot().plot()
and reproduce() methods.data.frame and can be
passed to appropriate statistical functions to summarize the data in the
plot.credible_interval(),
multi_credible_interval(), multi_hpd(),
dates_hiatus(), phases_gap(),
marginal_statistics(), and phase_statistics().
CredibleInterval()
-> credible_interval().phase_statistics() function is augmented with a
round_to parameter.multi_marginal_statistics().estimate_ranges() that can be
used to estimate the sensitivity of calibration results to different
model parameters.MultiHPD() that ignored the
roundingOfValue parameter.MarginalStatistics() that triggered an
error if the function was passed a constant MCMC chain.TempoPlot(): optimization of
the credible intervals as already done in
OccurrencePlot().MarginalPlot() and
adds a new function : MultiMarginalPlot().MarginalStatistics()
and adds a new function :
MultiMarginalMarginalStatistics().app_ArchaeoPhases()) that did not work in the previous
version.OccurrencePlot().ImportCSV() and a
new function for ‘BCal’ users called ImportCSV.BCal().MultiDatesPlot(). The graphic
is now done with ggplot2.TempoPlot() and
TempoActivityPlot() functions.app_ArchaeoPhases()).app_ArchaeoPhases()).ImportCSV() function in order
to import the raw MCMC generated by ‘BCal’ and to convert the MCMC
samples from the date format cal BP (in years before 1950) to the date
format BC/AD.Fishpond.RData().coda.mcmc() function.TempoPlot() function using
the package ggplot2.app_ArchaeoPhases()).coda.mcm() that creates a MCMC_list
in order to use the package coda.app_ArchaeoPhases() to call it from R.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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