Package 'ArchaeoChron'

Title: Bayesian Modeling of Archaeological Chronologies
Description: Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' (Plummer 2003). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.
Authors: Anne Philippe [aut, cre] , Marie-Anne Vibet [aut]
Maintainer: Anne Philippe <[email protected]>
License: GPL (>= 3)
Version: 0.2
Built: 2024-10-29 04:47:08 UTC
Source: https://github.com/ArchaeoStat/ArchaeoChron

Help Index

Bayesian Chronologies of Gaussian Dates

Description

Bayesian Chronologies of Gaussian Dates

Usage

chrono_Gauss(
  M,
  s,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Bayesian Chronologies of Gaussian Dates Using the Event Model

Description

Bayesian Chronologies of Gaussian Dates Using the Event Model

Usage

chronoEvents_Gauss(
  M,
  s,
  measurementsPerEvent,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
measurementsPerEvent

A ['numeric'] vector of giving the number of measurements per event.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Bayesian Chronologies of Gaussian Dates Using Oxcal Outlier Model

Description

Bayesian Chronologies of Gaussian Dates Using Oxcal Outlier Model

Usage

chronoOutliers_Gauss(
  M,
  s,
  outliersIndivVariance,
  outliersBernouilliProba,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
outliersIndivVariance

A ['numeric'] vector.
outliersBernouilliProba

A ['numeric'] vector.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Combine Gaussian Dates

Description

Combine Gaussian Dates

Usage

combination_Gauss(
  M,
  s,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Combine Gaussian Dates with Outliers

Description

Combine Gaussian Dates with Outliers

Usage

combinationWithOutliers_Gauss(
  M,
  s,
  outliersIndivVariance,
  outliersBernouilliProba,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
outliersIndivVariance

A ['numeric'] vector.
outliersBernouilliProba

A ['numeric'] vector.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Combine Gaussian Dates with a Random Effect

Description

Combine Gaussian Dates with a Random Effect

Usage

combinationWithRandomEffect_Gauss(
  M,
  s,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Cuers

Description

Cuers

Usage

cuers

Format

An object of class data.frame with 2 rows and 3 columns.

See Also

Other datasets: intcal13, marine13, shcal13, sunspot

Event Model for Radiocarbon Dates

Description

Event Model for Radiocarbon Dates

Usage

eventModel_C14(
  M,
  s,
  studyPeriodMin,
  studyPeriodMax,
  calibCurve = "intcal13",
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
calibCurve

A ['character'] string specifying the calibration curve to be used.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

Event Model for Gaussian Dates

Description

Event Model for Gaussian Dates

Usage

eventModel_Gauss(
  M,
  s,
  studyPeriodMin,
  studyPeriodMax,
  refYear = NULL,
  numberChains = 2,
  numberAdapt = 10000,
  numberUpdate = 10000,
  variable.names = c("theta"),
  numberSample = 50000,
  thin = 10
)

Arguments

M

A ['numeric'] vector of measurements.
s

A ['numeric'] vector of errors.
studyPeriodMin

A length-one ['numeric'] vector specifying the start time of the study period.
studyPeriodMax

A length-one ['numeric'] vector specifying the end time of the study period.
refYear

A ['numeric'] vector specifying the reference year. If 'NULL' (the default), AD years are expected.
numberChains

An ['integer'] giving the number of of parallel chains for the model (see [jags.model()]).
numberAdapt

An ['integer'] giving the number of iterations for adaptation (see [jags.model()]).
numberUpdate

An ['integer'] giving the number of iterations to update the model by.
variable.names

A ['character'] vector giving the names of variables to be monitored (see [coda.samples()]).
numberSample

An ['integer'] giving the number of iterations to monitor (see [coda.samples()]).
thin

An ['integer'] giving the thinning interval for monitors (see [coda.samples()]).

Value

An ['mcmc.list'][coda::mcmc.list()] object.

Author(s)

A. Philippe, M.-A. Vibet

IntCal13

Description

IntCal13

Usage

intcal13

Format

An object of class data.frame with 5141 rows and 3 columns.

See Also

Other datasets: cuers, marine13, shcal13, sunspot

Marine13

Description

Marine13

Usage

marine13

Format

An object of class data.frame with 4801 rows and 3 columns.

See Also

Other datasets: cuers, intcal13, shcal13, sunspot

ShCal13

Description

ShCal13

Usage

shcal13

Format

An object of class data.frame with 5141 rows and 3 columns.

See Also

Other datasets: cuers, intcal13, marine13, sunspot

Sunspot

Description

Sunspot

Usage

sunspot

Format

An object of class data.frame with 171 rows and 2 columns.

See Also

Other datasets: cuers, intcal13, marine13, shcal13