readme

Author: scollinselliott

README.Rmd

@@ -21,7 +21,7 @@ knitr::opts_chunk$set(
 
 The `R` package `eratosthenes` aims to provide a general, flexible toolkit for archaeological chronology-building by incorporating, computationally, all relevant sources of information on uncertain archaeological or historical dates. Archaeological dates are subject to relational conditions (via seriation or stratigraphic relationships) and absolute constraints (such as radiocarbon dates, datable artifacts, or other known historical events, as _termini post_ or _ante quos_), which prompt the use of a joint conditional probability density to convey those relationships. The date of any one event can then be marginalized from that full, joint conditional distribution.
 
-While software exists for calibrating and conditioning radiocarbon dates upon relative constraints, such as `OxCal` [@bronk_ramsey_bayesian_2009] and `BCal` [@buck_bcal_1999], as well as R packages `oxcAAR` [@hinz_oxcaar_2021], `Bchron` [@haslett_simple_2008], and `rcarbon` [@crema_spatio-temporal_2017], along with software for general chronological modeling like `Chronomodel` [@lanos_hierarchical_2017] and `ChronoLog` [@levy_chronological_2021], formal methods for dating artifacts and artifact types are lacking. One of the major goals of  `eratosthenes` is advance the synchronism of chronologies and the crafting of large-scale chronological relationships, which are heavily reliant upon artifact typologies. The package therefore facilitates the marginalization of dates of a type's production, use, and deposition. The method of sampling employed in `eratosthenes` involves a two-step process of Gibbs sampling, using consistent batch means (CBM) and Monte Carlo standard errors (MCSE) to determine convergence [@jones_fixed-width_2006,@flegal_markov_2008]. Finaly, `eratosthenes` provides tools for analyzing the impact of events on each other with the conditional structure stipulated by the investigator, by implementing a jackknife-style estimator of squared displacement (how much the date of one event shifts when another is omitted). Ancillary functions include checking for discrepancies in sequences of events and constraining optimal seriations to known sequences. `Rcpp` is required for faster Gibbs sampling.
+While software exists for calibrating and conditioning radiocarbon dates upon relative constraints, such as `OxCal` [@bronk_ramsey_bayesian_2009] and `BCal` [@buck_bcal_1999], as well as R packages `oxcAAR` [@hinz_oxcaar_2021], `Bchron` [@haslett_simple_2008], and `rcarbon` [@crema_spatio-temporal_2017], along with software for general chronological modeling like `Chronomodel` [@lanos_hierarchical_2017] and `ChronoLog` [@levy_chronological_2021], formal methods for dating artifacts and artifact types are lacking. One of the major goals of  `eratosthenes` is advance the synchronism of chronologies and the crafting of large-scale chronological relationships, which are heavily reliant upon artifact typologies. The package therefore facilitates the marginalization of dates of a type's production, use, and deposition. The method of sampling employed in `eratosthenes` involves a two-step process of Gibbs sampling, using consistent batch means (CBM) and Monte Carlo standard errors (MCSE) to determine convergence [@jones_fixed-width_2006; @flegal_markov_2008]. Finaly, `eratosthenes` provides tools for analyzing the impact of events on each other with the conditional structure stipulated by the investigator, by implementing a jackknife-style estimator of squared displacement (how much the date of one event shifts when another is omitted). Ancillary functions include checking for discrepancies in sequences of events and constraining optimal seriations to known sequences. `Rcpp` is required for faster Gibbs sampling.
 
 The package is named after Eratosthenes of Cyrene, author of the _Chronographiai_.
 

README.md

@@ -31,14 +31,15 @@ typologies. The package therefore facilitates the marginalization of
 dates of a type’s production, use, and deposition. The method of
 sampling employed in `eratosthenes` involves a two-step process of Gibbs
 sampling, using consistent batch means (CBM) and Monte Carlo standard
-errors (MCSE) to determine convergence Flegal, Haran, and Jones (2008).
-Finaly, `eratosthenes` provides tools for analyzing the impact of events
-on each other with the conditional structure stipulated by the
-investigator, by implementing a jackknife-style estimator of squared
-displacement (how much the date of one event shifts when another is
-omitted). Ancillary functions include checking for discrepancies in
-sequences of events and constraining optimal seriations to known
-sequences. `Rcpp` is required for faster Gibbs sampling.
+errors (MCSE) to determine convergence (Jones et al. 2006; Flegal,
+Haran, and Jones 2008). Finaly, `eratosthenes` provides tools for
+analyzing the impact of events on each other with the conditional
+structure stipulated by the investigator, by implementing a
+jackknife-style estimator of squared displacement (how much the date of
+one event shifts when another is omitted). Ancillary functions include
+checking for discrepancies in sequences of events and constraining
+optimal seriations to known sequences. `Rcpp` is required for faster
+Gibbs sampling.
 
 The package is named after Eratosthenes of Cyrene, author of the
 *Chronographiai*.