Package {palimpsestr}

Type:	Package
Title:	Probabilistic Decomposition of Archaeological Palimpsests
Version:	0.24.0
Description:	Probabilistic framework for the analysis of archaeological palimpsests based on the Stratigraphic Entanglement Field (SEF). Integrates spatial proximity, stratigraphic depth, chronological overlap, and cultural similarity to estimate latent depositional phases via diagonal Gaussian mixture Expectation-Maximisation (EM). Provides the Stratigraphic Entanglement Index (SEI), Excavation Stratigraphic Energy (ESE), and Palimpsest Dissolution Index (PDI) for quantifying depositional coherence, detecting intrusive finds, and measuring palimpsest formation. Includes simulation, diagnostics, phase-count selection, publication-quality plots, and Geographic Information System (GIS) export via 'sf'. Methods are described in Cocca (2026) https://github.com/enzococca/palimpsestr.
URL:	https://github.com/enzococca/palimpsestr
BugReports:	https://github.com/enzococca/palimpsestr/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.1.0)
Imports:	stats, utils, graphics, grDevices
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown, tinytex, sf, ggplot2, viridis, ggrepel, plotly, rlang, DBI, RSQLite, RPostgres, shiny, shinydashboard, DT, rcarbon (≥ 1.5.0), oxcAAR
Config/testthat/edition:	3
VignetteBuilder:	knitr
NeedsCompilation:	no
Config/roxygen2/version:	8.0.0
Packaged:	2026-07-25 15:45:43 UTC; enzo
Author:	Enzo Cocca [aut, cre]
Maintainer:	Enzo Cocca <enzo.ccc@gmail.com>
Repository:	CRAN
Date/Publication:	2026-07-25 17:00:02 UTC

palimpsestr: Probabilistic Decomposition of Archaeological Palimpsests

Description

Probabilistic framework for the analysis of archaeological palimpsests based on the Stratigraphic Entanglement Field (SEF).

Author(s)

Maintainer: Enzo Cocca enzo.ccc@gmail.com (ORCID)

Authors:

• Enzo Cocca enzo.ccc@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/enzococca/palimpsestr

• Report bugs at https://github.com/enzococca/palimpsestr/issues

Adjusted Rand Index

Description

Compares estimated phase assignments against known true labels using the Adjusted Rand Index (Hubert and Arabie, 1985). Values near 1 indicate perfect agreement; values near 0 indicate random agreement.

Usage

adjusted_rand_index(object, true_labels)

Arguments

Value

A single numeric value in [-1, 1].

See Also

confusion_matrix, fit_sef

Other validation: bootstrap_sef(), confusion_matrix(), cv_sef(), optimize_weights()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1, mixing = 0.05)
fit <- fit_sef(x, k = 3, seed = 1)
adjusted_rand_index(fit, x$true_phase)

Simulate an archaeological palimpsest dataset

Description

Generates a synthetic excavation dataset with known latent phases, controlled spatial clustering, and configurable inter-phase mixing.

Usage

archaeo_sim(n = 150, k = 3, seed = NULL, mixing = 0.08)

Arguments

Value

A data.frame with columns: id, x, y, z, context, date_min, date_max, class, taf_score, true_phase. The date bounds date_min and date_max are returned as integer-valued years (via floor()), consistent with archaeological dating conventions.

See Also

fit_sef for fitting the SEF model to the output.

Examples

easy <- archaeo_sim(n = 100, k = 3, mixing = 0.05, seed = 1)
table(easy$true_phase)

hard <- archaeo_sim(n = 200, k = 4, mixing = 0.50, seed = 1)
table(hard$true_phase)

Extract phase probability table

Description

Returns a data.frame combining dominant phase assignments, membership probabilities, entropy, local SEI, and energy.

Usage

as_phase_table(object)

Arguments

Value

A data.frame with one row per find.

See Also

predict_phase, phase_diagnostic_table

Other diagnostics: detect_intrusions(), ese(), pdi(), phase_composition(), phase_diagnostic_table(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
head(as_phase_table(fit))

Convert a ggplot to an interactive plotly widget

Description

Wraps ggplotly with archaeological tooltips showing find ID, context, phase probability, dating, class, entropy, and energy.

Usage

as_plotly(gg, tooltip = "text", ...)

Arguments

Value

A plotly htmlwidget object.

See Also

gg_phasefield, gg_entropy, gg_energy, gg_intrusions

Other plotting: gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
if (requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("plotly", quietly = TRUE)) {
  p <- as_plotly(gg_phasefield(fit))
}

Export high-SEI links as an sf LINESTRING layer

Description

Extracts the strongest pairwise SEI connections as sf line geometries.

Usage

as_sf_links(object, quantile_threshold = 0.9, crs = NA_integer_)

Arguments

Value

An sf object with columns from, to, sei, and geometry.

See Also

as_sf_phase, sei_matrix

Other GIS: as_sf_phase()

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  x <- archaeo_sim(n = 60, k = 2, seed = 1)
  fit <- fit_sef(x, k = 2)
  links <- as_sf_links(fit)
}

Convert a fitted model to an sf point layer

Description

Creates an sf point object with phase assignments and diagnostics for use in QGIS or spatial analysis.

Usage

as_sf_phase(object, crs = NA_integer_, dims = c("XY", "XYZ"))

Arguments

Value

An sf object.

See Also

as_sf_links, phase_diagnostic_table

Other GIS: as_sf_links()

Examples

if (requireNamespace("sf", quietly = TRUE)) {
  x <- archaeo_sim(n = 60, k = 2, seed = 1)
  fit <- fit_sef(x, k = 2)
  pts <- as_sf_phase(fit)
}

Bootstrap confidence intervals for SEF diagnostics

Description

Resamples the data with replacement, refits the model, and computes the distribution of key statistics (PDI, mean entropy, mean energy, ARI if true labels provided). Returns percentile confidence intervals.

Usage

bootstrap_sef(
  object,
  n_boot = 100,
  conf = 0.95,
  true_labels = NULL,
  verbose = TRUE
)

Arguments

Value

A data.frame with columns statistic, estimate, lower, upper, se.

See Also

fit_sef, pdi, adjusted_rand_index

Other validation: adjusted_rand_index(), confusion_matrix(), cv_sef(), optimize_weights()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2, seed = 1)
bootstrap_sef(fit, n_boot = 20)

Convert OxCal-calibrated dates to date_min/date_max columns

Description

Adapter that takes either an oxcAAR-calibrated dates object or a generic data.frame of calibrated ranges and returns a data.frame compatible with the chronology columns expected by fit_sef. It is the OxCal counterpart of chronology_from_rcarbon.

Usage

chronology_from_oxcal(
  x,
  method = c("hpd", "median_iqr", "weighted_mean"),
  hpd = 0.95,
  ids = NULL,
  bce_negative = TRUE
)

Arguments

Value

A data.frame with columns id, date_min, date_max, date_mid.

See Also

chronology_from_rcarbon, fit_sef

Other chronology: chronology_from_rcarbon()

Examples

d <- data.frame(id = c("a", "b"), start = c(-700, -50), end = c(-600, 100))
chronology_from_oxcal(d)

Convert calibrated radiocarbon dates to date_min/date_max columns

Description

Adapter that takes an rcarbon::CalDates object (produced by rcarbon::calibrate()) and returns a data.frame compatible with the chronology columns expected by fit_sef().

Usage

chronology_from_rcarbon(
  cal_dates,
  method = c("hpd", "median_iqr", "weighted_mean"),
  hpd = 0.95,
  ids = NULL,
  bce_negative = TRUE
)

Arguments

Value

A data.frame with columns id, date_min, date_max, date_mid.

See Also

fit_sef

Other chronology: chronology_from_oxcal()

Examples

## Not run: 
if (requireNamespace("rcarbon", quietly = TRUE)) {
  cal <- rcarbon::calibrate(x = c(2500, 2400), errors = c(30, 30))
  chronology_from_rcarbon(cal)
}

## End(Not run)

Compare multiple candidate phase counts

Description

Fits the SEF model for each value of K and returns a summary table with BIC, PDI, entropy, energy, and other diagnostics.

Usage

compare_k(data, k_values = 2:6, ...)

Arguments

Details

For each candidate K the table reports several model-selection quantities. The Bayesian Information Criterion (bic, -2\log\hat{L} + d\log n, where \hat{L} is the fitted mixture likelihood, d the number of free parameters and n the number of finds; Schwarz 1978) balances goodness of fit against model complexity and is the primary selector — lower is better. The Palimpsest Dissolution Index (pdi; see pdi) is a 0–1 measure of how cleanly the phases separate, and mean_entropy is the mean Shannon entropy of the soft assignments. The Integrated Completed Likelihood (icl) augments the BIC with the classification entropy (\mathrm{ICL} = \mathrm{BIC} + 2\sum_i H_i; Biernacki, Celeux & Govaert 2000), penalising overlapping phases so that it favours partitions with confident assignments. Statistical criteria should be combined with archaeological judgement: the BIC minimum is not necessarily the most interpretable partition.

Value

A data.frame with one row per K value.

References

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi:10.1214/aos/1176344136

Biernacki, C., Celeux, G., & Govaert, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(7), 719–725. doi:10.1109/34.865189

See Also

fit_sef, gg_compare_k, pdi

Other fitting: fit_sef(), reorder_phases(), sef_summary()

Examples

x <- archaeo_sim(n = 100, k = 3, seed = 1)
ck <- compare_k(x, k_values = 2:4)
print(ck)

Confusion matrix between estimated and true phases

Description

Cross-tabulates estimated phase assignments against known true labels. Phases are reordered to maximise diagonal agreement (Hungarian matching).

Usage

confusion_matrix(object, true_labels)

Arguments

Value

A matrix with estimated phases as rows and true phases as columns.

See Also

adjusted_rand_index

Other validation: adjusted_rand_index(), bootstrap_sef(), cv_sef(), optimize_weights()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1, mixing = 0.05)
fit <- fit_sef(x, k = 3, seed = 1)
confusion_matrix(fit, x$true_phase)

K-fold cross-validation for SEF model

Description

Splits the data into folds, fits on training folds, and evaluates log-likelihood on the held-out fold. Useful for comparing different K values or weight configurations.

Usage

cv_sef(data, k_values = 2:6, n_folds = 5, seed = 1, ...)

Arguments

Value

A data.frame with columns k, fold, train_loglik, test_loglik, train_pdi. The test_loglik carries the same Jacobian correction as optimize_weights, so it is comparable across weight configurations (not only across k); with default weights the correction is zero.

See Also

compare_k, fit_sef

Other validation: adjusted_rand_index(), bootstrap_sef(), confusion_matrix(), optimize_weights()

Examples

x <- archaeo_sim(n = 100, k = 3, seed = 1)
cv <- cv_sef(x, k_values = 2:4, n_folds = 3)
# Mean test log-likelihood per K
aggregate(test_loglik ~ k, data = cv, FUN = mean)

Demo dataset: compressed palimpsest

Description

Simulated dataset with 250 artefacts, 4 phases, and 50% mixing. Represents a heavily disturbed deposit where phases are difficult to separate.

Usage

demo_compressed

Format

A data.frame with 250 rows and 10 columns. See demo_easy for column descriptions.

Examples

data(demo_compressed)

ck <- compare_k(demo_compressed, k_values = 2:6)
print(ck)

Demo dataset: well-separated phases

Description

Simulated dataset with 150 artefacts, 3 phases, and 5% mixing. Represents a site where occupation phases are clearly distinguishable.

Usage

demo_easy

Format

A data.frame with 150 rows and 10 columns:

id

Artefact identifier

x, y

Planimetric coordinates (metres)

z

Depth (metres)

context

Stratigraphic unit label

date_min, date_max

Chronological interval (CE)

class

Cultural class (ceramic, lithic, bone, metal)

taf_score

Taphonomic disturbance score (0-1)

true_phase

Known phase assignment (for validation)

Examples

data(demo_easy)
fit <- fit_sef(demo_easy, k = 3)
plot_phasefield(fit)

Demo dataset: moderate palimpsest

Description

Simulated dataset with 200 artefacts, 3 phases, and 30% mixing. Represents a site with significant but resolvable depositional mixing.

Usage

demo_moderate

Format

A data.frame with 200 rows and 10 columns. See demo_easy for column descriptions.

Examples

data(demo_moderate)
fit <- fit_sef(demo_moderate, k = 3, tafonomy = "taf_score", context = "context")
summary(fit)

Detect potentially intrusive observations

Description

Returns a per-find intrusion probability. When the model was fitted with noise = TRUE, intrusion_prob is the posterior probability of the uniform noise component (a genuine outlier probability). Otherwise it is the heuristic composite of rescaled entropy, energy, and inverse local SEI.

Usage

detect_intrusions(object, envelope = c(0.05, 0.95), intrusion_threshold = 0.5)

Arguments

Value

A data.frame with columns id, intrusion_prob, direction (factor with levels older_than_context, in_context, younger_than_context), and chrono_gap (numeric, signed offset in years). The data.frame also carries an intrusion_type factor combining the intrusion magnitude with the direction: not_flagged, residual (flagged and older-than-context), latent_feature (flagged and younger-than-context), or outlier_in_context.

See Also

gg_intrusions, fit_sef, type_longevity

Other diagnostics: as_phase_table(), ese(), pdi(), phase_composition(), phase_diagnostic_table(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
di <- detect_intrusions(fit)
head(di[order(di$intrusion_prob, decreasing = TRUE), ])

Compute Excavation Stratigraphic Energy

Description

Measures local depositional disruption for each find by summing weighted dissimilarities with neighbours.

Usage

ese(
  data,
  coords = c("x", "y", "z"),
  chrono = c("date_min", "date_max"),
  class_col = "class",
  beta = c(1, 1, 1, 1),
  neighbourhood = NULL
)

Arguments

Value

A numeric vector of local energy values.

See Also

fit_sef, gg_energy

Other diagnostics: as_phase_table(), detect_intrusions(), pdi(), phase_composition(), phase_diagnostic_table(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 30, k = 2, seed = 1)
e <- ese(x)
summary(e)

Export all results to files

Description

Writes phase assignments, intrusion scores, US summary, and model summary to CSV files in the specified directory.

Usage

export_results(object, dir, format = "csv", prefix = "palimpsestr")

Arguments

Value

Invisibly returns a character vector of written file paths.

See Also

us_summary_table, as_phase_table

Other export: phase_transition_matrix(), us_summary_table()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2, context = "context")
export_results(fit, dir = tempdir())

Export a narrated SEF analysis report (PDF / DOCX)

Description

Assembles a complete report from a fitted sef_fit: the interpretive narrative of report_sef, all applicable gg_* diagnostic plots, and diagnostic tables (US summary, phase-transition matrix, per-US phase assignments, model statistics). The report is rendered to PDF and/or DOCX via an RMarkdown template.

Usage

export_sef_report(
  fit,
  file,
  format = c("pdf", "docx"),
  lang = c("it", "en"),
  title = NULL,
  site = NULL,
  compare_k = NULL,
  intrusion_threshold = 0.5,
  quiet = TRUE
)

Arguments

Details

A markdown narrative sidecar (<file>.md) is always written (used, for example, by the QGIS results panel). When pandoc is unavailable (as can happen when R is launched outside RStudio), the function degrades gracefully to that markdown narrative plus the figures saved as PNG, and tells you how to enable full rendering.

Value

(Invisibly) a character vector of the files written.

See Also

report_sef, fit_sef

Fit the Stratigraphic Entanglement Field model

Description

Estimates latent depositional phases from spatial, stratigraphic, chronological, and cultural evidence using diagonal Gaussian mixture EM.

Usage

fit_sef(
  data,
  coords = c("x", "y", "z"),
  chrono = c("date_min", "date_max"),
  class = "class",
  tafonomy = NULL,
  context = NULL,
  harris = NULL,
  k = 3,
  weights = c(ws = 1, wz = 1, wt = 1, wc = 1),
  seed = 1,
  em_iter = 100,
  em_tol = 1e-05,
  n_init = 5,
  chrono_precision = FALSE,
  taf_as_feature = FALSE,
  residuality = FALSE,
  class_scale = FALSE,
  subclass = NULL,
  var_structure = c("diagonal", "spherical"),
  class_model = c("multinomial", "gaussian"),
  class_smoothing = 1,
  chrono_uncertainty = FALSE,
  strat_dynamic = FALSE,
  strat_beta = 1,
  noise = FALSE,
  noise_prior = 0.05
)

Arguments

Value

An S3 object of class sef_fit.

See Also

archaeo_sim, compare_k, pdi, detect_intrusions

Other fitting: compare_k(), reorder_phases(), sef_summary()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
print(fit)


x <- archaeo_sim(n = 150, k = 3, seed = 42)
fit <- fit_sef(x, k = 3, tafonomy = "taf_score", context = "context")
summary(fit)

Bootstrap confidence interval plot

Description

Visualises bootstrap confidence intervals for key SEF diagnostics (PDI, entropy, energy, log-likelihood, and optionally ARI).

Usage

gg_bootstrap(bs_result)

Arguments

Value

A ggplot object.

See Also

bootstrap_sef

Other plotting: as_plotly(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Phase count selection diagnostics

Description

Three-panel plot showing BIC, PDI, and Mean Entropy for different K values.

Usage

gg_compare_k(ck)

Arguments

Value

A ggplot object.

See Also

compare_k

Other plotting: as_plotly(), gg_bootstrap(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Confusion matrix heatmap

Description

Plots a heatmap of the confusion matrix between estimated and known true phase assignments.

Usage

gg_confusion(object, true_labels)

Arguments

Value

A ggplot object.

See Also

confusion_matrix, adjusted_rand_index

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  x <- archaeo_sim(n = 80, k = 3, seed = 1, mixing = 0.05)
  fit <- fit_sef(x, k = 3, seed = 1)
  gg_confusion(fit, x$true_phase)
}

EM convergence trace

Description

Plots the log-likelihood at each EM iteration to verify convergence.

Usage

gg_convergence(object)

Arguments

Value

A ggplot object.

See Also

fit_sef

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  x <- archaeo_sim(n = 80, k = 3, seed = 1)
  fit <- fit_sef(x, k = 3)
  gg_convergence(fit)
}

Cross-validation diagnostic plot

Description

Plots mean test and training log-likelihood across K values from cv_sef output.

Usage

gg_cv(cv_result)

Arguments

Value

A ggplot object.

See Also

cv_sef, gg_compare_k

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Directional intrusion plot

Description

Diverging lollipop of the signed chronological offset (chrono_gap) for finds classified as residual (older-than-context) or latent (younger-than-context) by detect_intrusions.

Usage

gg_direction(object, top_n = 20)

Arguments

Value

A ggplot object. When every dated find is in context (e.g. unit-tied chronology) an explanatory placeholder plot is returned.

See Also

detect_intrusions, gg_intrusions

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2, context = "context")
gg_direction(fit)

Excavation Stratigraphic Energy map

Description

Shows ESE values across space. High energy indicates zones where neighbouring artefacts are dissimilar (depositional disruption).

Usage

gg_energy(object, xlabel = "Easting (m)", ylabel = "Northing (m)")

Arguments

Value

A ggplot object.

See Also

gg_entropy, as_plotly for interactive version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Spatial entropy map

Description

Shows Shannon entropy of phase probabilities across the excavation area. High entropy = uncertain phase assignment (possible palimpsest zone).

Usage

gg_entropy(object, xlabel = "Easting (m)", ylabel = "Northing (m)")

Arguments

Value

A ggplot object.

See Also

plot_entropy for base R version, as_plotly for interactive version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Intrusion probability map

Description

Maps the probability that each find is an intrusion (displaced or redeposited). Top suspects are circled and labelled.

Usage

gg_intrusions(
  object,
  top_n = 5,
  xlabel = "Easting (m)",
  ylabel = "Northing (m)"
)

Arguments

Value

A ggplot object.

See Also

detect_intrusions, as_plotly for interactive version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Gantt-style plot of type longevity

Description

Visualises the output of type_longevity() as a horizontal Gantt chart: one segment per class spanning longevity_min to longevity_max, ordered by longevity_min, coloured by dominant_phase.

Usage

gg_longevity(longevity_table, fit = NULL)

Arguments

Value

A ggplot object.

See Also

type_longevity

Examples

x <- archaeo_sim(n = 90, k = 3, seed = 1)
fit <- fit_sef(x, k = 3, context = "context")
tl <- type_longevity(fit)
if (requireNamespace("ggplot2", quietly = TRUE)) gg_longevity(tl, fit)

Overlay SEF results on excavation plan geometries

Description

Displays SEF analysis results directly on the excavation plan. Polygons represent stratigraphic units; points represent individual finds.

Usage

gg_map(
  object,
  geometries,
  layer = c("phase", "entropy", "energy", "intrusion"),
  show_labels = TRUE,
  show_points = TRUE,
  xlabel = NULL,
  ylabel = NULL
)

Arguments

Value

A ggplot object.

See Also

load_geometries, as_sf_phase

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Intrusion ranking plot

Description

Non-spatial ranking of the per-find intrusion probability (complements gg_intrusions, which is the map). Uses the noise-component posterior when the model was fitted with noise = TRUE, otherwise the heuristic composite score.

Usage

gg_outliers(object, threshold = 0.5, top_n = 20)

Arguments

Value

A ggplot object.

See Also

detect_intrusions, gg_intrusions

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2, noise = TRUE)
gg_outliers(fit)

Per-phase class composition plot

Description

Visualises the estimated per-phase categorical class profile (object$cat_prob) from a multinomial-class fit.

Usage

gg_phase_composition(object, type = c("heatmap", "bar"), top_n = NULL)

Arguments

Value

A ggplot object.

See Also

phase_composition, fit_sef

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
gg_phase_composition(fit)

Vertical phase profile

Description

Plots finds along the depth (z) axis, coloured by phase assignment, to visualise the stratigraphic ordering of phases.

Usage

gg_phase_profile(object)

Arguments

Value

A ggplot object.

See Also

phase_transition_matrix

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

if (requireNamespace("ggplot2", quietly = TRUE)) {
  x <- archaeo_sim(n = 80, k = 3, seed = 1)
  fit <- fit_sef(x, k = 3)
  gg_phase_profile(fit)
}

Phase assignment map

Description

Plots artefact positions coloured by dominant phase, with point size proportional to assignment confidence.

Usage

gg_phasefield(object, xlabel = "Easting (m)", ylabel = "Northing (m)")

Arguments

Value

A ggplot object.

See Also

plot_phasefield for base R version, as_plotly for interactive version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Within-unit phase coherence plot

Description

Shows, per stratigraphic unit, whether the finds were assigned to a single phase (coherent) or split across several (split). A coherent assignment is expected because a unit is one depositional event. An optional second fit (e.g. a class_model = "gaussian" fit) is shown side by side.

Usage

gg_unit_coherence(object, compare = NULL)

Arguments

Value

A ggplot object.

See Also

fit_sef, us_summary_table

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3, context = "context")
gg_unit_coherence(fit)

Weight sensitivity heatmap

Description

Plots the mean test log-likelihood across weight configurations from optimize_weights output.

Usage

gg_weights(opt_result)

Arguments

Value

A ggplot object.

See Also

optimize_weights

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), plot_energy(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Generate stratigraphic penalty matrix from context depth ordering

Description

Infers vertical ordering between stratigraphic units from the mean depth of finds in each context, and builds a penalty matrix that discourages finds from different vertical zones being assigned to the same phase.

Usage

harris_from_contexts(
  data,
  z_col = "z",
  context_col = "context",
  penalty_scale = 0.5,
  exclude_contexts = NULL
)

Arguments

Value

An n \times n symmetric penalty matrix.

Verticality assumption

This function encodes a verticality rule: it assumes that the mean depth of a context is a proxy for its relative chronological position (deeper = earlier) and penalises same-phase assignment of finds whose contexts differ in depth rank. The assumption holds for sub-horizontally stratified deposits but is frequently violated in real excavations — for inclined deposits and slope collapses, and especially for the fills of cuts (pits, ditches, post-holes) and for single contexts that contain material from several chronological periods. In such cases the depth-rank penalty conflates genuinely problematic configurations with perfectly normal archaeological features. Two escape hatches are provided: pass the affected contexts to exclude_contexts to exempt them from the penalty, or skip this helper entirely and supply an explicitly recorded Harris matrix via read_harris.

See Also

fit_sef, read_harris

Other harris: read_harris(), validate_phases_harris()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
H <- harris_from_contexts(x, z_col = "z", context_col = "context")
dim(H)
# Exempt a known multi-period fill from the verticality rule:
H2 <- harris_from_contexts(x, exclude_contexts = "SU_3")

Launch the palimpsestr Shiny Dashboard

Description

Opens an interactive Shiny application for loading data, fitting the SEF model, and exploring results through plots, tables, and maps.

Usage

launch_app()

Details

The app requires the following packages (installed but not imported by palimpsestr): shiny, shinydashboard, DT. For interactive plots install plotly. For database connections install DBI plus RSQLite or RPostgres. For GIS maps install sf.

Value

Launches the Shiny app (does not return).

Examples

## Not run: 
launch_app()

## End(Not run)

Load excavation geometries from file or database

Description

Reads stratigraphic unit polygons from Shapefile, GeoPackage, GeoJSON, or a PostGIS database for use with gg_map.

Usage

load_geometries(
  source,
  layer = NULL,
  query = NULL,
  us_column = "us",
  crs = NULL
)

Arguments

Value

An sf object with a us column.

See Also

gg_map, read_db

Other data-import: read_db(), read_pyarchinit()

Compute local SEI values

Description

Aggregates the SEI matrix by row, yielding a per-observation measure of total depositional coherence with all other finds.

Usage

local_sei(sei_mat)

Arguments

Value

A numeric vector of length nrow(sei_mat).

See Also

sei_matrix

Other SEI: sei_matrix(), sei_sparse()

Examples

x <- archaeo_sim(n = 30, k = 2, seed = 1)
S <- sei_matrix(x)
lsei <- local_sei(S)
summary(lsei)

Estimate optimal SEI weights via cross-validation

Description

Tests a grid of weight configurations and selects the one that maximises the mean held-out log-likelihood across folds. This provides a data-driven alternative to manual weight specification.

Usage

optimize_weights(
  data,
  k,
  weight_grid = NULL,
  n_folds = 3,
  seed = 1,
  verbose = TRUE,
  var_structure = "spherical",
  ...
)

Arguments

Details

Since v0.14.0 the weights scale the feature dimensions and therefore enter the mixture likelihood (in earlier versions they only affected the SEI matrix, so the cross-validated objective was invariant to them). Two technical points make the comparison meaningful. First, free diagonal variances absorb any rescaling of the feature columns, so weight selection is performed under var_structure = "spherical", where the weights define the relative precision of the dimensions and are identifiable; the returned best_weights are therefore meant to be used with fit_sef(..., var_structure = "spherical"). Second, held-out log-likelihoods are mapped back to the common unweighted feature scale via a Jacobian correction (n_{test} \sum_d \log w_d) so that configurations with different weights are directly comparable.

Value

A list with components:

best_weights

Named numeric vector of optimal weights.

results

Data.frame with all tested configurations and their scores.

See Also

fit_sef, cv_sef

Other validation: adjusted_rand_index(), bootstrap_sef(), confusion_matrix(), cv_sef()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
opt <- optimize_weights(x, k = 3, n_folds = 3)
opt$best_weights

Compute Palimpsest Dissolution Index

Description

Measures global phase separability as 1 - \bar{H} / \log(K), where \bar{H} is the mean per-find Shannon entropy of the soft phase assignments and K the number of phases. Values close to 1 indicate well-separated phases (low entropy); values near 0 indicate a compressed, heavily mixed palimpsest. As a rule of thumb, PDI > 0.7 indicates well-resolved phases. The index is an interpretable, dimensionless summary reported alongside the BIC (see compare_k) during model selection; unlike the BIC it does not penalise model complexity, so it should not be used on its own to choose K.

Usage

pdi(object)

Arguments

Value

A single numeric value between 0 and 1.

See Also

fit_sef, compare_k

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), phase_composition(), phase_diagnostic_table(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
pdi(fit)

Per-phase class composition

Description

Returns the estimated categorical class profile of each phase from a fit made with class_model = "multinomial": row j gives the probability that a find assigned to phase j belongs to each material class. This is the model-based counterpart of cross-tabulating finds by phase and class, and is directly interpretable (e.g. "phase 2 is dominated by amphorae and fine ware").

Usage

phase_composition(object)

Arguments

Value

A data.frame with a phase column and one column per class level; each row sums to 1.

See Also

fit_sef, as_phase_table

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), pdi(), phase_diagnostic_table(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
phase_composition(fit)

Return a compact diagnostic table

Description

Combines the input data with dominant phase, phase probabilities, entropy, local SEI, and energy in a single data.frame.

Usage

phase_diagnostic_table(object)

Arguments

Value

A data.frame with all input columns plus diagnostics.

See Also

as_phase_table, as_sf_phase

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), pdi(), phase_composition(), predict_phase(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
pdt <- phase_diagnostic_table(fit)
names(pdt)

Phase vertical transition matrix

Description

Computes how often finds from phase i are found directly above finds from phase j in the vertical sequence, revealing the stratigraphic ordering of phases.

Usage

phase_transition_matrix(object)

Arguments

Value

A K \times K matrix where entry [i,j] counts transitions from phase i (above) to phase j (below).

See Also

us_summary_table

Other export: export_results(), us_summary_table()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
phase_transition_matrix(fit)

Plot local Excavation Stratigraphic Energy (base R)

Description

Plot local Excavation Stratigraphic Energy (base R)

Usage

plot_energy(object)

Arguments

Value

Invisibly returns the object.

See Also

gg_energy for the ggplot2 version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_entropy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
plot_energy(fit)

Plot entropy across space (base R)

Description

Plot entropy across space (base R)

Usage

plot_entropy(object)

Arguments

Value

Invisibly returns the object.

See Also

gg_entropy for the ggplot2/plotly version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_phasefield(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
plot_entropy(fit)

Plot dominant phase assignment (base R)

Description

Plot dominant phase assignment (base R)

Usage

plot_phasefield(object)

Arguments

Value

Invisibly returns the object.

See Also

gg_phasefield for the ggplot2/plotly version.

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_sei_profile()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
plot_phasefield(fit)

Plot ordered SEI profile (base R)

Description

Plot ordered SEI profile (base R)

Usage

plot_sei_profile(object)

Arguments

Value

Invisibly returns the object.

See Also

sei_matrix, local_sei

Other plotting: as_plotly(), gg_bootstrap(), gg_compare_k(), gg_confusion(), gg_convergence(), gg_cv(), gg_direction(), gg_energy(), gg_entropy(), gg_intrusions(), gg_map(), gg_outliers(), gg_phase_composition(), gg_phase_profile(), gg_phasefield(), gg_unit_coherence(), gg_weights(), plot_energy(), plot_entropy(), plot_phasefield()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
plot_sei_profile(fit)

Predict phase probabilities

Description

Convenience alias for as_phase_table.

Usage

predict_phase(object)

Arguments

Value

A data.frame with probabilities and diagnostics.

See Also

as_phase_table

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), pdi(), phase_composition(), phase_diagnostic_table(), recommend_setup(), type_longevity()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
head(predict_phase(fit))

Read archaeological data from a SQL database

Description

Loads find data from any DBI-compatible database and maps columns to the format expected by fit_sef.

Usage

read_db(
  conn,
  query = NULL,
  table = NULL,
  col_id = "id",
  col_x = "x",
  col_y = "y",
  col_z = "z",
  col_context = "context",
  col_date_min = "date_min",
  col_date_max = "date_max",
  col_class = "class",
  col_taf = NULL,
  schema = "custom",
  sito = NULL
)

Arguments

Value

A data.frame ready for fit_sef.

See Also

fit_sef, load_geometries

Other data-import: load_geometries(), read_pyarchinit()

Read Harris Matrix from CSV edge list

Description

Reads a CSV file with columns from, to, and optionally weight, and converts it to an n \times n penalty matrix aligned with the find-level data.

Usage

read_harris(file, contexts, default_weight = 1)

Arguments

Value

An n \times n penalty matrix.

See Also

harris_from_contexts, fit_sef

Other harris: harris_from_contexts(), validate_phases_harris()

Read finds from a pyArchInit database for palimpsest analysis

Description

Reads finds from a pyArchInit database and assembles a data.frame ready for fit_sef. Finds come from inventario_materiali_table (materials) and/or pottery_table (pottery), selectable via source. A find uses its own plan coordinates when it is plotted as a point in pyarchinit_reperti; otherwise it inherits its stratigraphic unit's coordinates (centroid of the US polygon). Chronology comes from us_table, while elevation follows archaeological practice (see Details).

Usage

read_pyarchinit(
  con,
  us_geometry = NULL,
  sito = NULL,
  source = c("both", "materials", "pottery"),
  date_labels = NULL,
  taf = NULL,
  us_geom_field = NULL,
  synthetic_coords = TRUE,
  quote_table = "pyarchinit_quote",
  chronology_table = "palimpsest_chronology",
  pottery_class = c("ware", "material", "form"),
  reperti_table = "pyarchinit_reperti",
  reperti_geometry = NULL
)

Arguments

Details

Elevation (z) is sourced, in order of precedence:

1. the find's own quota_usm (materials only);

2. the mean US elevation from pyarchinit_quote (quota_q), joined on (sito, area, us) with an (sito, us) fallback;

3. 0 when neither is available.

The deprecated us_table.quota_abs form field is not used. Pottery has no elevation column, so it always inherits its US elevation. Dating is resolved from us_table.datazione (falling back to the find's own dating) by an internal archaeological-date parser.

Value

A data.frame with id, x, y, z, date_min, date_max, class, context, taf_score (plus find_source when source = "both"), ready for fit_sef.

See Also

read_db, fit_sef

Other data-import: load_geometries(), read_db()

Inspect a dataset and recommend fit_sef() flags

Description

Examines the structural properties of a palimpsestr-ready dataset and returns a set of recommended fit_sef() options based on simple heuristics. Useful when the dataset has subtle properties (US-centroid coordinates, US-tied chronology, imbalanced classes, informative taphonomic scores) that would otherwise be discovered only by trial and error.

Usage

recommend_setup(
  data,
  coords = c("x", "y", "z"),
  chrono = c("date_min", "date_max"),
  class_col = "class",
  tafonomy = NULL,
  context = NULL
)

Arguments

Value

An S3 object of class sef_setup with:

• recommendations: named list with logical flags class_scale, taf_as_feature, chrono_precision, residuality.

• diagnostics: list of structural properties detected in the data (number of classes, singleton classes, whether coordinates are at unit centroid, whether chronology is US-tied, whether taf is informative, etc.).

• warnings: character vector of caveats (e.g. directional intrusion will be uninformative because chronology is US-tied).

• suggested_call: string with the recommended fit_sef() invocation.

See Also

fit_sef

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), pdi(), phase_composition(), phase_diagnostic_table(), predict_phase(), type_longevity()

Examples

x <- archaeo_sim(n = 100, k = 3, seed = 1)
recommend_setup(x, context = "context")


data(villa_romana)
recommend_setup(villa_romana, context = "context", tafonomy = "taf_score")

Reorder phases by mean depth

Description

Relabels phases so that phase 1 corresponds to the deepest (oldest) stratum and phase K to the shallowest (most recent). This ensures consistent, interpretable phase numbering across different runs.

Usage

reorder_phases(object)

Arguments

Value

A sef_fit object with reordered phases.

See Also

fit_sef

Other fitting: compare_k(), fit_sef(), sef_summary()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
fit <- reorder_phases(fit)

Generate a textual interpretive report for a SEF model

Description

Produces a structured Markdown report covering phase composition, intrusion detection, stratigraphic unit purity, and recommendations. Available in English and Italian.

Usage

report_sef(object, lang = c("en", "it"), file = NULL)

Arguments

Value

Character string with the report text (invisibly).

See Also

fit_sef, sef_summary

Examples

x <- archaeo_sim(n = 100, k = 3, seed = 1)
fit <- fit_sef(x, k = 3)
report_sef(fit, lang = "en")

Compact summary for a fitted SEF model

Description

Returns a named list with global diagnostics and phase counts.

Usage

sef_summary(object)

Arguments

Value

A named list.

See Also

fit_sef, pdi

Other fitting: compare_k(), fit_sef(), reorder_phases()

Examples

x <- archaeo_sim(n = 60, k = 2, seed = 1)
fit <- fit_sef(x, k = 2)
sef_summary(fit)

Compute the Stratigraphic Entanglement Index matrix

Description

Builds an n \times n symmetric matrix quantifying pairwise depositional coherence from spatial, vertical, temporal, and cultural evidence.

Usage

sei_matrix(
  data,
  coords = c("x", "y", "z"),
  chrono = c("date_min", "date_max"),
  class_col = "class",
  weights = c(ws = 1, wz = 1, wt = 1, wc = 1),
  eps = 1e-09,
  z_floor = 0.25,
  max_dist = NULL
)

Arguments

Value

A symmetric numeric matrix with zero diagonal.

Note

SEI values are normalised within each dataset. Absolute SEI values are not directly comparable across different excavations or datasets of different sizes. Use SEI for within-dataset ranking and relative comparisons only.

See Also

local_sei, fit_sef

Other SEI: local_sei(), sei_sparse()

Examples

x <- archaeo_sim(n = 30, k = 2, seed = 1)
S <- sei_matrix(x)
dim(S)

Compute SEI matrix with automatic sparsification

Description

Wrapper around sei_matrix that automatically sets max_dist when the dataset exceeds a size threshold, using the 25th percentile of pairwise distances as the cutoff.

Usage

sei_sparse(
  data,
  coords = c("x", "y", "z"),
  chrono = c("date_min", "date_max"),
  class_col = "class",
  weights = c(ws = 1, wz = 1, wt = 1, wc = 1),
  eps = 1e-09,
  z_floor = 0.25,
  n_threshold = 1000
)

Arguments

Value

A numeric matrix (same as sei_matrix).

See Also

sei_matrix

Other SEI: local_sei(), sei_matrix()

Examples

x <- archaeo_sim(n = 50, k = 2, seed = 1)
S <- sei_sparse(x)

Summarise a fitted SEF model

Description

Summarise a fitted SEF model

Usage

## S3 method for class 'sef_fit'
summary(object, ...)

Arguments

Value

A named list.

Posterior-weighted type longevity

Description

For each cultural class (or sub-class), returns the temporal envelope of the depositional phases in which the class has non-negligible posterior membership. The longevity is the union of the per-phase chronological envelopes for those phases whose mean posterior weight (averaged over the finds of that class) exceeds posterior_threshold.

Usage

type_longevity(
  object,
  class_col = NULL,
  posterior_threshold = 0.1,
  sub_class = FALSE
)

Arguments

Value

A data.frame with columns class, longevity_min, longevity_max, longevity_span, dominant_phase, n_finds, and a list-column weight_matrix containing the per-phase posterior weights (length K).

See Also

detect_intrusions, gg_longevity

Other diagnostics: as_phase_table(), detect_intrusions(), ese(), pdi(), phase_composition(), phase_diagnostic_table(), predict_phase(), recommend_setup()

Examples

x <- archaeo_sim(n = 90, k = 3, seed = 1)
fit <- fit_sef(x, k = 3, context = "context")
type_longevity(fit)

Summary table per stratigraphic unit

Description

Aggregates finds by context (US), reporting the dominant phase, purity (proportion of finds in dominant phase), mean entropy, mean energy, and intrusion count.

Usage

us_summary_table(object)

Arguments

Value

A data.frame with one row per stratigraphic unit.

See Also

export_results, phase_diagnostic_table

Other export: export_results(), phase_transition_matrix()

Examples

x <- archaeo_sim(n = 80, k = 3, seed = 1)
fit <- fit_sef(x, k = 3, context = "context")
us_summary_table(fit)

Validate phase assignments against stratigraphic ordering

Description

Checks whether the estimated phases follow the expected vertical ordering within each stratigraphic unit pair.

Usage

validate_phases_harris(object)

Arguments

Value

A data.frame with one row per context pair, indicating whether the dominant phase ordering is consistent with depth.

See Also

harris_from_contexts, us_summary_table

Other harris: harris_from_contexts(), read_harris()

Examples

x <- archaeo_sim(n = 60, k = 3, seed = 1)
fit <- fit_sef(x, k = 3, context = "context")
validate_phases_harris(fit)

Poggio Gramignano Roman Villa excavation dataset

Description

Real archaeological dataset from the site of Poggio Gramignano (Lugnano in Teverina, TR, Italy), a multi-period Roman villa with an annexed Late Antique infant cemetery. The dataset contains 615 inventoried finds from 54 stratigraphic units spanning from the Pre-Roman period (7th–4th c. BCE) through the Late Antique (5th–6th c. CE).

Usage

villa_romana

Format

A data.frame with 615 rows and 9 variables:

id

Unique find identifier (VRPG_0001 to VRPG_0615)

x

Easting coordinate (metres, EPSG:3004)

y

Northing coordinate (metres, EPSG:3004)

z

Mean elevation of the stratigraphic unit (metres a.s.l.)

context

Stratigraphic unit label (e.g. US_76, US_117)

date_min

Start of chronological interval (BCE as negative, CE as positive)

date_max

End of chronological interval

class

Material class (e.g. Reperto Ceramico, Reperto Anforaceo)

taf_score

Taphonomic integrity score (0 = fully disturbed, 1 = pristine in situ)

Details

Chronological data derives from the site periodisation. Coordinates are US centroids in Monte Mario / Italy zone 2 (EPSG:3004). Elevation (z) is the mean of recorded quotes per US. Taphonomic scores were assigned by the excavator based on depositional context (primary, secondary, redeposition, backfill, disturbance).

Source

Data from the pyArchInit database of Poggio Gramignano (VRPG 3004). Taphonomic scores compiled by Roberto Montagnetti.

Examples

data(villa_romana)
str(villa_romana)
table(villa_romana$class)