Package 'ThSQCA'

Compute Fiss Core/Peripheral Classification for Sweep Results

Description

Takes an existing threshold-sweep result object (produced by otSweep, ctSweepS, ctSweepM, or dtSweep) and augments it with Fiss (2011) core/peripheral classification.

Takes an existing threshold-sweep result object (produced by otSweep, ctSweepS, ctSweepM, or dtSweep) and augments it with Fiss (2011) core/peripheral classification.

Usage

compute_fiss_core(result, conditions = NULL)

compute_fiss_core(result, conditions = NULL)

Arguments

result	A sweep result object with $details and $settings slots (e.g., from otSweep(..., return_details = TRUE)).
conditions	Character vector. Condition names (used for consistent row ordering in charts). If NULL, extracted automatically.

Details

The classification requires that:

• The sweep was run with include = "?" (to allow parsimonious computation)

• return_details = TRUE was used (truth tables must be stored)

• dir.exp was specified (i.e., the sweep produced intermediate solutions — core/peripheral is only meaningful when comparing parsimonious vs intermediate)

For each threshold in the result, this function:

1. Retrieves the intermediate solution already stored in result$details.

2. Re-runs QCA::minimize() on the same truth table with dir.exp = NULL to obtain the parsimonious solution.

3. Compares the two solutions: conditions appearing in both are core; conditions appearing only in the intermediate solution are peripheral.

The classification requires that:

• The sweep was run with include = "?" (to allow parsimonious computation)

• return_details = TRUE was used (truth tables must be stored)

• dir.exp was specified (i.e., the sweep produced intermediate solutions — core/peripheral is only meaningful when comparing parsimonious vs intermediate)

For each threshold in the result, this function:

1. Retrieves the intermediate solution already stored in result$details.

2. Re-runs QCA::minimize() on the same truth table with dir.exp = NULL to obtain the parsimonious solution.

3. Compares the two solutions: conditions appearing in both are core; conditions appearing only in the intermediate solution are peripheral.

Value

The original result object with an additional $fiss_core slot: a named list keyed by threshold (character), each entry containing:

• parsim_expression — parsimonious solution expression

• interm_expression — intermediate solution expression

• classification — data frame with columns term_idx, term_expr, condition, status, type

The original result object with an additional $fiss_core slot: a named list keyed by threshold (character), each entry containing:

• parsim_expression — parsimonious solution expression

• interm_expression — intermediate solution expression

• classification — data frame with columns term_idx, term_expr, condition, status, type

References

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

See Also

generate_fiss_chart

generate_fiss_chart

Examples

library(ThSQCA)
data(sample_data)

# Step 1: Run intermediate sweep (dir.exp required)
res <- otSweep(
  dat        = sample_data,
  outcome    = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX       = c(X1 = 7, X2 = 7, X3 = 7),
  include    = "?",
  dir.exp    = c(1, 1, 1),
  return_details = TRUE
)

# Step 2: Augment with Fiss core/peripheral classification
res_fiss <- compute_fiss_core(res, conditions = c("X1", "X2", "X3"))

# Step 3: Generate Fiss-style chart
cat(generate_fiss_chart(res_fiss, symbol_set = "unicode"))



library(ThSQCA)
data(sample_data)

# Step 1: Run intermediate sweep (dir.exp required)
res <- otSweep(
  dat        = sample_data,
  outcome    = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX       = c(X1 = 7, X2 = 7, X3 = 7),
  include    = "?",
  dir.exp    = c(1, 1, 1),
  return_details = TRUE
)

# Step 2: Augment with Fiss core/peripheral classification
res_fiss <- compute_fiss_core(res, conditions = c("X1", "X2", "X3"))

# Step 3: Generate Fiss-style chart
cat(generate_fiss_chart(res_fiss, symbol_set = "unicode"))

---

Generate configuration chart from paths (simple interface)

Description

A simpler interface for generating configuration charts when you have paths directly (without a full QCA solution object).

Usage

config_chart_from_paths(
  paths,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja"),
  condition_order = NULL,
  n_sol = 1L,
  solution_note = TRUE,
  solution_note_style = c("simple", "detailed"),
  epi_list = NULL
)

Arguments

paths	Character vector. Paths in QCA notation (e.g., "A*B*~C").
symbol_set	Character. One of "unicode", "ascii", or "latex".
language	Character. "en" for English, "ja" for Japanese.
condition_order	Character vector. Optional ordering of conditions.
n_sol	Integer. Number of equivalent solutions. If > 1, a note is added explaining that multiple solutions exist and M1 is shown. Default is 1.
solution_note	Logical. Whether to add solution note when n_sol > 1. Default is TRUE.
solution_note_style	Character. "simple" or "detailed". Default is "simple".
epi_list	Character vector. Essential prime implicants for detailed notes. Only used when solution_note_style = "detailed".

Value

Character string containing Markdown-formatted table.

Examples

# Simple usage with paths
paths <- c("A*B", "A*C*~D", "B*E")
chart <- config_chart_from_paths(paths)
cat(chart)

# With ASCII symbols
chart <- config_chart_from_paths(paths, symbol_set = "ascii")
cat(chart)

# With multiple solution note
chart <- config_chart_from_paths(paths, n_sol = 2)
cat(chart)

# With detailed note including EPIs
chart <- config_chart_from_paths(
  paths, n_sol = 2,
  solution_note_style = "detailed",
  epi_list = c("A*B")
)
cat(chart)

---

Generate configuration chart for multiple solutions (simple interface)

Description

Generates separate configuration charts for multiple solutions.

Usage

config_chart_multi_solutions(
  solutions,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja"),
  condition_order = NULL,
  show_epi = FALSE
)

Arguments

solutions	List of character vectors. Each element is a vector of paths for one solution.
symbol_set	Character. One of "unicode", "ascii", or "latex".
language	Character. "en" for English, "ja" for Japanese.
condition_order	Character vector. Optional ordering of conditions.
show_epi	Logical. Whether to identify and display Essential Prime Implicants (EPIs) in the note. Default is FALSE.

Value

Character string containing Markdown-formatted tables.

Examples

# Multiple solutions
solutions <- list(
  c("A*B", "C"),
  c("A*B", "D"),
  c("A*C")
)
chart <- config_chart_multi_solutions(solutions)
cat(chart)

# With EPI identification
chart <- config_chart_multi_solutions(solutions, show_epi = TRUE)
cat(chart)

---

MCTS–QCA: Multi-condition threshold sweep

Description

Performs a grid search over thresholds of multiple X variables. For each combination of thresholds in sweep_list, the outcome Y and all X variables are binarized, and a crisp-set QCA is executed.

Usage

ctSweepM(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_list,
  thrY,
  pre_calibrated = NULL,
  dir.exp = NULL,
  include = "",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

dat	Data frame containing the outcome and condition variables.
outcome	Character. Outcome variable name. Supports negation with tilde prefix (e.g., "~Y") following QCA package conventions.
conditions	Character vector. Names of condition variables.
sweep_list	Named list. Each element is a numeric vector of candidate thresholds for the corresponding X. Names must match conditions. Variables listed in pre_calibrated do not need a sweep_list entry.
thrY	Numeric. Threshold for Y (fixed).
pre_calibrated	Character vector or NULL. Names of condition variables that have been pre-calibrated (e.g., via QCA::calibrate()) and should be passed through to QCA::truthTable() without binarization. These variables must contain values in the [0, 1] range. Variables not listed here will be binarized using sweep_list thresholds as usual. Default is NULL (all variables binarized). It is recommended to sweep variables on their original (raw) scale rather than as pre-calibrated fuzzy values, because raw-scale thresholds are easier to interpret substantively.
dir.exp	Directional expectations for minimize. If NULL (default), no directional expectations are applied. To compute the intermediate solution, specify a numeric vector (1, 0, or -1 for each condition). Example: dir.exp = c(1, 1, 1) for three conditions all expected to contribute positively.
include	Inclusion rule for minimize. "" (default, QCA compatible) computes the complex solution without logical remainders. Use "?" to include logical remainders for parsimonious (with dir.exp = NULL) or intermediate solutions (with dir.exp specified).
incl.cut	Consistency cutoff for truthTable.
n.cut	Frequency cutoff for truthTable.
pri.cut	PRI cutoff for minimize.
extract_mode	Character. How to handle multiple solutions: "first" (default), "all", or "essential". See qca_extract for details.
return_details	Logical. If TRUE (default), returns both summary and detailed objects for use with generate_report().
Yvar	Deprecated. Use outcome instead.
Xvars	Deprecated. Use conditions instead.

Value

If return_details = FALSE, a data frame with columns:

• combo_id — index of the threshold combination

• threshold — character string summarizing thresholds, e.g. "X1=6, X2=7, X3=7"

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-combination list of combo_id, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# === Three Types of QCA Solutions ===

# Quick demonstration with 2 conditions
sweep_list <- list(X1 = 7, X2 = 7)

# 1. Complex Solution (default, QCA compatible)
result_comp <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7
  # include = "" (default), dir.exp = NULL (default)
)
head(result_comp$summary)

# 2. Parsimonious Solution (include = "?")
result_pars <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7,
  include = "?"  # Include logical remainders
)
head(result_pars$summary)

# 3. Intermediate Solution (include = "?" + dir.exp)
result_int <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7,
  include = "?",
  dir.exp = c(1, 1)  # Positive expectations
)
head(result_int$summary)

# === Threshold Sweep Example ===

# Using 2 conditions and 2 threshold levels
sweep_list <- list(
  X1 = 6:7,
  X2 = 6:7
)

# Run multiple condition threshold sweep (complex solutions by default)
result_quick <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7
)
head(result_quick$summary)

# Run with negated outcome (~Y)
result_neg <- ctSweepM(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2"),
  sweep_list = sweep_list,
  thrY = 7
)
head(result_neg$summary)


# Full multi-condition analysis (27 combinations)
sweep_list_full <- list(
  X1 = 6:8,
  X2 = 6:8,
  X3 = 6:8
)

result_full <- ctSweepM(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_list = sweep_list_full,
  thrY = 7
)
head(result_full$summary)

---

CTS–QCA: Single-condition threshold sweep

Description

Performs a threshold sweep for one focal condition X. For each threshold in sweep_range, the outcome Y and all X variables are binarized using user-specified thresholds, and a crisp-set QCA is executed.

Usage

ctSweepS(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_var,
  sweep_range,
  thrY,
  thrX_default = 7,
  pre_calibrated = NULL,
  dir.exp = NULL,
  include = "",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

dat	Data frame containing the outcome and condition variables.
outcome	Character. Outcome variable name. Supports negation with tilde prefix (e.g., "~Y") following QCA package conventions.
conditions	Character vector. Names of condition variables.
sweep_var	Character. Name of the condition to be swept. Must be one of conditions.
sweep_range	Numeric vector. Candidate thresholds for sweep_var.
thrY	Numeric. Threshold for Y (fixed).
thrX_default	Numeric. Default threshold for non-swept X variables. Variables listed in pre_calibrated do not require this threshold.
pre_calibrated	Character vector or NULL. Names of condition variables that have been pre-calibrated (e.g., via QCA::calibrate()) and should be passed through to QCA::truthTable() without binarization. These variables must contain values in the [0, 1] range. Variables not listed here will be binarized using thrX_default as usual. Default is NULL (all variables binarized). It is recommended to sweep variables on their original (raw) scale rather than as pre-calibrated fuzzy values, because raw-scale thresholds are easier to interpret substantively.
dir.exp	Directional expectations for minimize. If NULL (default), no directional expectations are applied. To compute the intermediate solution, specify a numeric vector (1, 0, or -1 for each condition). Example: dir.exp = c(1, 1, 1) for three conditions all expected to contribute positively.
include	Inclusion rule for minimize. "" (default, QCA compatible) computes the complex solution without logical remainders. Use "?" to include logical remainders for parsimonious (with dir.exp = NULL) or intermediate solutions (with dir.exp specified).
incl.cut	Consistency cutoff for truthTable.
n.cut	Frequency cutoff for truthTable.
pri.cut	PRI cutoff for minimize.
extract_mode	Character. How to handle multiple solutions: "first" (default), "all", or "essential". See qca_extract for details.
return_details	Logical. If TRUE (default), returns both summary and detailed objects for use with generate_report().
Yvar	Deprecated. Use outcome instead.
Xvars	Deprecated. Use conditions instead.

Value

If return_details = FALSE, a data frame with columns:

• threshold — swept threshold for sweep_var

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-threshold list of threshold, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# === Three Types of QCA Solutions ===

# 1. Complex Solution (default, QCA compatible)
result_comp <- ctSweepS(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 7,
  thrY = 7,
  thrX_default = 7
  # include = "" (default), dir.exp = NULL (default)
)
head(result_comp$summary)

# 2. Parsimonious Solution (include = "?")
result_pars <- ctSweepS(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 7,
  thrY = 7,
  thrX_default = 7,
  include = "?"  # Include logical remainders
)
head(result_pars$summary)

# 3. Intermediate Solution (include = "?" + dir.exp)
result_int <- ctSweepS(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 7,
  thrY = 7,
  thrX_default = 7,
  include = "?",
  dir.exp = c(1, 1, 1)  # All conditions expected positive
)
head(result_int$summary)

# === Threshold Sweep Example ===

# Run single condition threshold sweep on X3 (complex solutions by default)
result <- ctSweepS(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 6:8,
  thrY = 7,
  thrX_default = 7
)
head(result$summary)

# Run with negated outcome (~Y)
result_neg <- ctSweepS(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_var = "X3",
  sweep_range = 6:8,
  thrY = 7,
  thrX_default = 7
)
head(result_neg$summary)

---

DTS–QCA: Two-dimensional X–Y threshold sweep

Description

Sweeps thresholds for multiple X variables and the outcome Y jointly. For each combination of X thresholds and each candidate Y threshold, the data are binarized and a crisp-set QCA is executed.

Usage

dtSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_list_X,
  sweep_range_Y,
  pre_calibrated = NULL,
  dir.exp = NULL,
  include = "",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

dat	Data frame containing the outcome and condition variables.
outcome	Character. Outcome variable name. Supports negation with tilde prefix (e.g., "~Y") following QCA package conventions.
conditions	Character vector. Names of condition variables.
sweep_list_X	Named list. Each element is a numeric vector of candidate thresholds for the corresponding X. Variables listed in pre_calibrated do not need a sweep_list_X entry.
sweep_range_Y	Numeric vector. Candidate thresholds for Y.
pre_calibrated	Character vector or NULL. Names of condition variables that have been pre-calibrated (e.g., via QCA::calibrate()) and should be passed through to QCA::truthTable() without binarization. These variables must contain values in the [0, 1] range. Variables not listed here will be binarized using sweep_list_X thresholds as usual. Default is NULL (all variables binarized). It is recommended to sweep variables on their original (raw) scale rather than as pre-calibrated fuzzy values, because raw-scale thresholds are easier to interpret substantively.
dir.exp	Directional expectations for minimize. If NULL (default), no directional expectations are applied. To compute the intermediate solution, specify a numeric vector (1, 0, or -1 for each condition). Example: dir.exp = c(1, 1, 1) for three conditions all expected to contribute positively.
include	Inclusion rule for minimize. "" (default, QCA compatible) computes the complex solution without logical remainders. Use "?" to include logical remainders for parsimonious (with dir.exp = NULL) or intermediate solutions (with dir.exp specified).
incl.cut	Consistency cutoff for truthTable.
n.cut	Frequency cutoff for truthTable.
pri.cut	PRI cutoff for minimize.
extract_mode	Character. How to handle multiple solutions: "first" (default), "all", or "essential". See qca_extract for details.
return_details	Logical. If TRUE (default), returns both summary and detailed objects for use with generate_report().
Yvar	Deprecated. Use outcome instead.
Xvars	Deprecated. Use conditions instead.

Value

If return_details = FALSE, a data frame with columns:

• combo_id — index of threshold combination

• thrY — threshold for Y

• thrX — character summary of X thresholds

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — list of runs with combo_id, thrY, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# === Three Types of QCA Solutions ===

# Quick demonstration with 2 conditions
sweep_list_X <- list(X1 = 7, X2 = 7)
sweep_range_Y <- 7

# 1. Complex Solution (default, QCA compatible)
result_comp <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y
  # include = "" (default), dir.exp = NULL (default)
)
head(result_comp$summary)

# 2. Parsimonious Solution (include = "?")
result_pars <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y,
  include = "?"  # Include logical remainders
)
head(result_pars$summary)

# 3. Intermediate Solution (include = "?" + dir.exp)
result_int <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y,
  include = "?",
  dir.exp = c(1, 1)  # Positive expectations
)
head(result_int$summary)

# === Threshold Sweep Example ===

# Using 2 conditions and 2 threshold levels
sweep_list_X <- list(
  X1 = 6:7,
  X2 = 6:7
)
sweep_range_Y <- 6:7

# Run dual threshold sweep (complex solutions by default)
result_quick <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2"),
  sweep_list_X = sweep_list_X,
  sweep_range_Y = sweep_range_Y
)
head(result_quick$summary)


# Full analysis with 3 conditions (81 combinations)
sweep_list_X_full <- list(
  X1 = 6:8,
  X2 = 6:8,
  X3 = 6:8
)
sweep_range_Y_full <- 6:8

result_full <- dtSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_list_X = sweep_list_X_full,
  sweep_range_Y = sweep_range_Y_full
)
head(result_full$summary)

---

Extract and format terms from solutions

Description

Extracts individual terms from solution expressions and returns formatted unique terms.

Usage

extract_terms(solutions, var_names, use_tilde = TRUE)

Arguments

solutions	Character vector. Solution expressions.
var_names	Character vector. Variable names used in the analysis.
use_tilde	Logical. If TRUE, negation is represented as ~VAR.

Value

List with:

• all_terms — all terms (with duplicates)

• unique_terms — unique terms

• n_total — total term count

• n_unique — unique term count

Examples

var_names <- c("X1", "X2", "X3")
solutions <- c("X1*X2 + X3", "X1*X2 + X1*X3")
extract_terms(solutions, var_names)

---

Format a QCA solution expression

Description

Formats a complete solution expression (multiple terms joined by +).

Usage

format_qca_solution(solution, var_names, use_tilde = TRUE)

Arguments

solution	Character. A solution expression (e.g., "KSPRVT + ~KPRPRD").
var_names	Character vector. Variable names used in the analysis.
use_tilde	Logical. If TRUE, negation is represented as ~VAR.

Value

Character. The formatted solution expression.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
format_qca_solution("KSPRVT + ~KPRPRD + RCM", var_names)
# Returns: "KSP*RVT + ~KPR*PRD + RCM"

---

Format multiple QCA solutions

Description

Formats a vector of solution expressions.

Usage

format_qca_solutions(solutions, var_names, use_tilde = TRUE)

Arguments

solutions	Character vector. Solution expressions from minimize().
var_names	Character vector. Variable names used in the analysis.
use_tilde	Logical. If TRUE, negation is represented as ~VAR.

Value

Character vector. Formatted solution expressions.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
solutions <- c("KSPRVT + RCM", "~KPRPRD")
format_qca_solutions(solutions, var_names)

---

Format a single QCA term

Description

Inserts * between variables in a term where it may have been omitted.

Usage

format_qca_term(term, var_names, use_tilde = TRUE)

Arguments

term	Character. A single term (e.g., "KSPRVT" or "~KPR*PRD").
var_names	Character vector. Variable names used in the analysis.
use_tilde	Logical. If TRUE, negation is represented as ~VAR. If FALSE, negation is represented as lowercase (e.g., var).

Value

Character. The formatted term with * between all variables.

Examples

var_names <- c("KSP", "KPR", "PRD", "RVT", "RCM")
format_qca_term("KSPRVTRCM", var_names)
# Returns: "KSP*RVT*RCM"

format_qca_term("~KPRPRD", var_names)
# Returns: "~KPR*PRD"

---

Generate Configuration Chart from QCA Solution

Description

Creates a Markdown-formatted configuration chart (Fiss-style table) from QCA minimization results. Supports single solution with multiple paths, and multiple solutions (displayed as separate tables).

Usage

generate_config_chart(
  sol,
  symbol_set = c("unicode", "ascii", "latex"),
  include_metrics = TRUE,
  language = c("en", "ja"),
  condition_order = NULL
)

Arguments

sol	A solution object returned by QCA::minimize(), or a list containing solution information.
symbol_set	Character. One of "unicode", "ascii", or "latex". Default is "unicode".
include_metrics	Logical. Whether to include consistency/coverage metrics in the table. Default is TRUE.
language	Character. "en" for English, "ja" for Japanese. Default is "en".
condition_order	Character vector. Optional ordering of conditions in the table rows. If NULL, conditions are ordered as they appear in paths.

Value

Character string containing Markdown-formatted table(s).

---

Generate cross-threshold configuration chart from sweep results

Description

Creates a configuration chart from threshold sweep results. Supports two levels of aggregation: solution-term level (Fiss-style, default) and threshold-level summary.

Usage

generate_cross_threshold_chart(
  result,
  conditions = NULL,
  symbol_set = c("unicode", "ascii", "latex"),
  chart_level = c("term", "summary"),
  language = c("en", "ja")
)

Arguments

result	A result object from any Sweep function (otSweep, ctSweepS, ctSweepM, or dtSweep).
conditions	Character vector. Condition names for row ordering. If NULL, automatically extracted from expressions.
symbol_set	Character. One of "unicode", "ascii", or "latex". Default is "unicode".
chart_level	Character. Chart aggregation level: "term" (default) produces solution-term level charts following Fiss (2011) notation, where each column represents one prime implicant. "summary" produces threshold-level summaries where each column represents one threshold, aggregating all configurations.
language	Character. "en" for English, "ja" for Japanese.

Value

Character string containing Markdown-formatted table.

Examples

data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

# Solution-term level, Fiss-style (default)
chart <- generate_cross_threshold_chart(result, c("X1", "X2", "X3"))
cat(chart)

# Threshold-level summary
chart <- generate_cross_threshold_chart(result, c("X1", "X2", "X3"),
                                         chart_level = "summary")
cat(chart)

---

Generate Fiss-Style Configuration Chart from Sweep Results

Description

Produces a Markdown-formatted configuration chart following Fiss (2011), using four symbols to distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (present in intermediate solution only).

Produces a Markdown-formatted configuration chart following Fiss (2011), using four symbols to distinguish core conditions (present in both parsimonious and intermediate solutions) from peripheral conditions (present in intermediate solution only).

Usage

generate_fiss_chart(
  result,
  conditions = NULL,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja")
)

generate_fiss_chart(
  result,
  conditions = NULL,
  symbol_set = c("unicode", "ascii", "latex"),
  language = c("en", "ja")
)

Arguments

result	Sweep result augmented by compute_fiss_core.
conditions	Character vector. Condition names (row order). If NULL, extracted from stored settings.
symbol_set	Character. One of "unicode" (default), "ascii", or "latex".
language	Character. "en" (default) or "ja".

Details

Call compute_fiss_core first to augment the sweep result.

Call compute_fiss_core first to augment the sweep result.

Value

Character string: Markdown-formatted Fiss configuration chart.

Character string: Markdown-formatted Fiss configuration chart.

References

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420.

See Also

compute_fiss_core

compute_fiss_core

Examples

data(sample_data)
res <- otSweep(
  dat = sample_data, outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7),
  include = "?", dir.exp = c(1, 1, 1),
  return_details = TRUE
)
res_fiss <- compute_fiss_core(res, conditions = c("X1", "X2", "X3"))
cat(generate_fiss_chart(res_fiss))
cat(generate_fiss_chart(res_fiss, symbol_set = "latex"))
cat(generate_fiss_chart(res_fiss, language = "ja"))



data(sample_data)
res <- otSweep(
  dat = sample_data, outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7),
  include = "?", dir.exp = c(1, 1, 1),
  return_details = TRUE
)
res_fiss <- compute_fiss_core(res, conditions = c("X1", "X2", "X3"))
cat(generate_fiss_chart(res_fiss))
cat(generate_fiss_chart(res_fiss, symbol_set = "latex"))
cat(generate_fiss_chart(res_fiss, language = "ja"))

---

Generate Markdown Report for QCA Analysis

Description

Creates a markdown report from QCA analysis results. Supports two formats: "full" (comprehensive) and "simple" (for manuscripts).

Usage

generate_report(
  result,
  output_file = file.path(tempdir(), "qca_report.md"),
  format = c("full", "simple"),
  title = "QCA Analysis Report",
  dat = NULL,
  desc_vars = NULL,
  include_chart = TRUE,
  chart_symbol_set = c("unicode", "ascii", "latex"),
  chart_level = c("term", "summary"),
  solution_note = TRUE,
  solution_note_style = c("simple", "detailed"),
  solution_note_lang = c("en", "ja"),
  include_fiss_core = FALSE,
  include_raw_output = TRUE
)

Arguments

result	A result object from any Sweep function with return_details = TRUE.
output_file	Character. Path to output markdown file.
format	Character. Report format: "full" or "simple".
title	Character. Report title.
dat	Optional data frame. Original data for descriptive statistics.
desc_vars	Optional character vector. Variables for descriptive statistics. If NULL and dat is provided, uses Yvar and Xvars from params.
include_chart	Logical. If TRUE (default), includes configuration charts (Fiss-style tables) in the report for each threshold.
chart_symbol_set	Character. Symbol set for configuration charts: "unicode" (default), "ascii", or "latex".
chart_level	Character. Chart aggregation level: "term" (default) produces solution-term level charts following Fiss (2011) notation, where each column represents one prime implicant (sufficient configuration). This format is recommended for academic publications. "summary" produces threshold-level summaries where each column represents one threshold, aggregating all configurations.
solution_note	Logical. If TRUE (default), adds a note when multiple equivalent solutions exist explaining that M1 is shown.
solution_note_style	Character. Style of solution note: "simple" (default) or "detailed" (includes EPIs).
solution_note_lang	Character. Language for solution notes: "en" (default) or "ja".
include_fiss_core	Logical. If TRUE and result$fiss_core exists (i.e., compute_fiss_core has been run), the configuration charts use full Fiss (2011) four-symbol notation distinguishing core (present in both parsimonious and intermediate solutions) from peripheral conditions (intermediate only). If FALSE (default) or if fiss_core data is absent, the standard two-symbol chart is used.
include_raw_output	Logical. If TRUE (default), includes the raw QCA package output (print(sol)) for each threshold for verification purposes.

Value

Invisibly returns the path to the generated report.

Examples

data(sample_data)
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX,
  return_details = TRUE
)

# With descriptive statistics and configuration charts
generate_report(result, file.path(tempdir(), "my_report.md"), format = "full", 
                dat = sample_data, include_chart = TRUE)

# Without configuration charts
generate_report(result, file.path(tempdir(), "my_report.md"), format = "simple",
                include_chart = FALSE)

# With Fiss-style term-level charts (default, recommended for publications)
generate_report(result, file.path(tempdir(), "my_report.md"), format = "full")

# With threshold-level summary charts
generate_report(result, file.path(tempdir(), "my_report.md"), format = "full",
                chart_level = "summary")

---

Generate solution note for multiple solutions

Description

Creates a note explaining that multiple equivalent solutions exist and that the displayed configuration is based on M1.

Usage

generate_solution_note(
  n_sol,
  epi_list = NULL,
  style = c("simple", "detailed"),
  language = c("en", "ja"),
  format = c("markdown", "latex")
)

Arguments

n_sol	Integer. Number of solutions.
epi_list	Character vector. Essential prime implicants (NULL to omit).
style	Character. "simple" or "detailed".
language	Character. "en" or "ja".
format	Character. "markdown" or "latex".

Value

Character string of the note, or empty string if n_sol <= 1.

Examples

# Simple note
generate_solution_note(2, style = "simple")

# Detailed note with EPIs
generate_solution_note(3, epi_list = c("A*B", "C"), style = "detailed")

# Japanese
generate_solution_note(2, style = "simple", language = "ja")

---

Identify Essential Prime Implicants from multiple solutions

Description

Finds terms that appear in ALL solutions (EPIs) versus terms that appear in only some solutions (SPIs).

Usage

identify_epi(solutions)

Arguments

solutions

List of solution vectors. Each element is a character vector of terms for one solution.

Value

List with:

• epi — Essential prime implicants (in all solutions)

• spi — Selective prime implicants (in some solutions)

• n_solutions — Number of solutions

Examples

solutions <- list(
  c("A*B", "C", "D"),
  c("A*B", "C", "E"),
  c("A*B", "C", "F")
)
result <- identify_epi(solutions)
# result$epi = c("A*B", "C")
# result$spi = c("D", "E", "F")

---

OTS–QCA: Outcome threshold sweep

Description

Sweeps the threshold of the outcome Y while keeping the thresholds of all X conditions fixed.

Usage

otSweep(
  dat,
  outcome = NULL,
  conditions = NULL,
  sweep_range,
  thrX,
  pre_calibrated = NULL,
  dir.exp = NULL,
  include = "",
  incl.cut = 0.8,
  n.cut = 1,
  pri.cut = 0,
  extract_mode = c("first", "all", "essential"),
  return_details = TRUE,
  Yvar = NULL,
  Xvars = NULL
)

Arguments

dat	Data frame containing the outcome and condition variables.
outcome	Character. Outcome variable name. Supports negation with tilde prefix (e.g., "~Y") following QCA package conventions.
conditions	Character vector. Names of condition variables.
sweep_range	Numeric vector. Candidate thresholds for Y.
thrX	Named numeric vector. Fixed thresholds for X variables. Names must match the conditions that require binarization. Variables listed in pre_calibrated do not need a thrX entry.
pre_calibrated	Character vector or NULL. Names of condition variables that have been pre-calibrated (e.g., via QCA::calibrate()) and should be passed through to QCA::truthTable() without binarization. These variables must contain values in the [0, 1] range. Variables not listed here will be binarized using thrX thresholds as usual. Default is NULL (all variables binarized). It is recommended to sweep variables on their original (raw) scale rather than as pre-calibrated fuzzy values, because raw-scale thresholds are easier to interpret substantively.
dir.exp	Directional expectations for minimize. If NULL (default), no directional expectations are applied. To compute the intermediate solution, specify a numeric vector (1, 0, or -1 for each condition). Example: dir.exp = c(1, 1, 1) for three conditions all expected to contribute positively.
include	Inclusion rule for minimize. "" (default, QCA compatible) computes the complex solution without logical remainders. Use "?" to include logical remainders for parsimonious (with dir.exp = NULL) or intermediate solutions (with dir.exp specified).
incl.cut	Consistency cutoff for truthTable.
n.cut	Frequency cutoff for truthTable.
pri.cut	PRI cutoff for minimize.
extract_mode	Character. How to handle multiple solutions: "first" (default), "all", or "essential". See qca_extract for details.
return_details	Logical. If TRUE (default), returns both summary and detailed objects for use with generate_report().
Yvar	Deprecated. Use outcome instead.
Xvars	Deprecated. Use conditions instead.

Value

If return_details = FALSE, a data frame with columns:

• thrY — threshold for Y

• expression — minimized solution expression

• inclS — solution consistency

• covS — solution coverage

• (additional columns depending on extract_mode)

If return_details = TRUE, a list with:

• summary — the data frame above

• details — per-Y-threshold list of thrY, thrX_vec, truth_table, solution

Examples

# Load sample data
data(sample_data)

# Set fixed thresholds for conditions
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

# === Three Types of QCA Solutions ===

# 1. Complex Solution (default, QCA compatible)
#    Does not use logical remainders (most conservative)
result_comp <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX
  # include = "" (default), dir.exp = NULL (default)
)
head(result_comp$summary)

# 2. Parsimonious Solution (include = "?")
#    Uses logical remainders without directional expectations
result_pars <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?"  # Include logical remainders
)
head(result_pars$summary)

# 3. Intermediate Solution (include = "?" + dir.exp)
#    Uses logical remainders with directional expectations
result_int <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?",
  dir.exp = c(1, 1, 1)  # All conditions expected positive
)
head(result_int$summary)

# === Threshold Sweep Example ===

# Sweep with complex solutions (default)
result_sweep <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX
)
head(result_sweep$summary)

# Run with negated outcome (~Y)
# Analyzes conditions for Y < threshold
result_neg <- otSweep(
  dat = sample_data,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = thrX
)
head(result_neg$summary)

---

Print Fiss core/peripheral summary for a single threshold

Description

Displays which conditions are core and which are peripheral at a given threshold, in a human-readable format.

Displays which conditions are core and which are peripheral at a given threshold, in a human-readable format.

Usage

print_fiss_summary(result, thr_key, language = c("en", "ja"))

print_fiss_summary(result, thr_key, language = c("en", "ja"))

Arguments

result	Sweep result augmented by compute_fiss_core.
thr_key	Character or numeric. Threshold key (e.g., "7" or 7).
language	Character. "en" or "ja".

Value

Invisibly returns the classification data frame for thr_key.

Invisibly returns the classification data frame for thr_key.

---

Print method for ThSQCA results

Description

Displays a concise overview of ThSQCA analysis results.

Usage

## S3 method for class 'tsqca_result'
print(x, ...)

## S3 method for class 'otSweep_result'
print(x, ...)

## S3 method for class 'dtSweep_result'
print(x, ...)

## S3 method for class 'ctSweepS_result'
print(x, ...)

## S3 method for class 'ctSweepM_result'
print(x, ...)

Arguments

x	A ThSQCA result object returned by one of the sweep functions.
...	Additional arguments (ignored).

Value

Invisibly returns x.

Examples

data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)
print(result)

---

Sample dataset for ThSQCA examples

Description

A small artificial dataset with variables:

Y

Outcome (numeric)

X1

Condition 1

X2

Condition 2

X3

Condition 3

Usage

sample_data

Format

A data frame with 80 rows and 4 variables.

---

Summary method for ThSQCA results

Description

Displays detailed results table with solution formulas and fit measures.

Usage

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

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

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

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

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

Arguments

object	A ThSQCA result object returned by one of the sweep functions.
...	Additional arguments (ignored).

Value

Invisibly returns object.

Examples

data(sample_data)
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)
summary(result)

---