bury() gained new span and degree arguments.as_phases().aion::TimeIntervals-class for time ranges representation.ArchaeoPhases v2.0 brings a comprehensive package rewrite. You can install the 1.x releases from the CRAN archives:
# install.packages("remotes")
remotes::install_version("ArchaeoPhases", version = "1.8")
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.