Introduction

Threshold-Sweep QCA (TS-QCA) is a framework for systematically exploring how different threshold settings affect the results of crisp-set Qualitative Comparative Analysis (QCA).
In crisp-set QCA, the researcher must choose thresholds to binarize:

the outcome Y, and
the conditions X.

Small changes in these thresholds may lead to substantial differences in truth tables and minimized solutions.

TS-QCA provides:

a systematic way to vary thresholds,
a transparent way to evaluate sensitivity,
and a reproducible workflow for robustness assessment.

The TSQCA package implements four sweep methods:

Method	What varies	What stays fixed	Purpose
CTS–QCA	One X threshold	Y + other Xs	Evaluate influence of a single condition
MCTS–QCA	Multiple X thresholds	Y	Explore combinations of X thresholds
OTS–QCA	Y threshold	All Xs	Assess robustness to Y calibration
DTS–QCA	X and Y thresholds	None	Full 2D sensitivity analysis

Scope: This package focuses on sufficiency analysis—identifying condition combinations that are sufficient for an outcome. Necessity analysis (whether a condition is required for an outcome) involves different logical structures and evaluation metrics, and is planned for future versions.

Data Requirements

TS-QCA assumes:

TS-QCA handles any numeric data (continuous, interval, or ordinal scales). Pre-calibration is NOT required because raw scores (e.g., 1–85 scale) are binarized directly at specified thresholds.
Ordinal data can be analyzed directly, enabling analyses that are difficult with fsQCA (which requires continuous membership values).
Thresholds can be any numeric value (integer or real).
Missing values should be handled before running sweeps.

Example dataset structure:

library(TSQCA)
data("sample_data")
dat <- sample_data
str(dat)
#> 'data.frame':    80 obs. of  4 variables:
#>  $ Y : int  8 4 5 7 2 2 7 5 3 8 ...
#>  $ X1: int  7 2 6 8 8 9 8 8 1 5 ...
#>  $ X2: int  7 5 8 4 0 5 8 4 4 2 ...
#>  $ X3: int  1 6 6 5 3 4 5 3 6 8 ...

Define outcome and conditions:

outcome  <- "Y"
conditions <- c("X1", "X2", "X3")

Working with Mixed Data Types

Handling Binary and Continuous Variables

In real-world social science research, datasets often contain both binary variables (e.g., gender, yes/no responses) and continuous variables (e.g., sales, satisfaction scores). When using TSQCA with such mixed data, special attention is required.

Key Principles

Do NOT sweep binary variables — they are already binarized (0/1)
Use threshold = 1 for binary variables to preserve their original values
Explicitly specify thresholds for each variable in sweep_list

Why Threshold = 1 for Binary Variables?

The internal qca_bin() function uses the rule x >= thr for binarization:

If x = 0: 0 >= 1 → FALSE → 0 (preserved)
If x = 1: 1 >= 1 → TRUE → 1 (preserved)

This ensures that binary variables remain unchanged during the binarization process.

Practical Example: Mixed Data

Suppose your dataset has:

X1: Gender (0 = Male, 1 = Female) — binary variable
X2: Product Quality Score (0-10) — continuous variable
X3: Store Atmosphere Score (0-10) — continuous variable

When using ctSweepM():

# CORRECT: Specify threshold explicitly for each variable
sweep_list <- list(
  X1 = 1,      # Binary variable: use threshold 1
  X2 = 6:8,    # Continuous: sweep thresholds
  X3 = 6:8     # Continuous: sweep thresholds
)

res_mixed <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  dir.exp        = c(1, 1, 1)
)

This explores 1 × 3 × 3 = 9 threshold combinations, treating X1 as a fixed binary condition while sweeping X2 and X3.

Common Mistake

# WRONG: Using sweep range for binary variables
sweep_list <- list(
  X1 = 6:8,    # All values become 0 (since 0 < 6 and 1 < 6)
  X2 = 6:8,
  X3 = 6:8
)

If you accidentally specify X1 = 6:8, both 0 and 1 will fail the >= 6 condition, making all X1 values become 0. This destroys the information in your binary variable.

Best Practice

Always examine your data structure before setting up threshold sweeps:

# Check variable ranges
summary(dat[, c("X1", "X2", "X3")])

# Identify binary variables (only 0 and 1)
sapply(dat[, c("X1", "X2", "X3")], function(x) {
  unique_vals <- sort(unique(x))
  if (length(unique_vals) == 2 && all(unique_vals == c(0, 1))) {
    "Binary (use threshold = 1)"
  } else {
    paste("Continuous (range:", min(x), "-", max(x), ")")
  }
})

CTS–QCA: Single-Condition Sweep (`ctSweepS`)

CTS–QCA varies the threshold for one X condition, keeping the others fixed.

sweep_var   <- "X3"   # Condition (X) whose threshold is swept
sweep_range <- 6:9    # Candidate threshold values to evaluate

thrY         <- 7     # Outcome (Y) threshold (fixed)
thrX_default <- 7     # Threshold for other X conditions (fixed)

res_cts <- ctSweepS(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_var      = sweep_var,
  sweep_range    = sweep_range,
  thrY           = thrY,
  thrX_default   = thrX_default,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_cts)
#> CTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  threshold expression inclS  covS n_solutions
#>          6      X1*X2 1.000 0.303           1
#>          7 X3 + X1*X2 0.906 0.879           1
#>          8 X3 + X1*X2 1.000 0.818           1
#>          9 X3 + X1*X2 1.000 0.515           1

MCTS–QCA: Multiple X Sweep (`ctSweepM`)

MCTS–QCA evaluates all combinations of thresholds for multiple X conditions.

# Create a sweep list specifying thresholds for each condition
sweep_list <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

res_mcts <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_mcts)
#> MCTS-QCA Summary
#> ================ 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>         threshold combo_id     expression inclS  covS n_solutions
#>  X1=6, X2=6, X3=6        1          X1*X2 0.833 0.455           1
#>  X1=7, X2=6, X3=6        2          X1*X2 1.000 0.394           1
#>  X1=6, X2=7, X3=6        3      X1*X2*~X3 0.818 0.273           1
#>  X1=7, X2=7, X3=6        4          X1*X2 1.000 0.303           1
#>  X1=6, X2=6, X3=7        5             X3 0.864 0.576           1
#>  X1=7, X2=6, X3=7        6 ~X1*X3 + X1*X2 0.931 0.818           1
#>  X1=6, X2=7, X3=7        7             X3 0.864 0.576           1
#>  X1=7, X2=7, X3=7        8     X3 + X1*X2 0.906 0.879           1

OTS–QCA: Outcome Sweep (`otSweep`)

OTS–QCA varies only the threshold of Y, keeping X thresholds fixed.

res_ots <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = c(X1 = 7, X2 = 7, X3 = 7),
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_ots)
#> OTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  thrY  expression inclS  covS n_solutions
#>     6  X3 + X1*X2 0.906 0.853           1
#>     7  X3 + X1*X2 0.906 0.879           1
#>     8 No solution    NA    NA           0

DTS–QCA: Two-Dimensional Sweep (`dtSweep`)

DTS–QCA varies both X thresholds and Y thresholds, creating a full 2D grid.

sweep_list_dts_X <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

sweep_range_dts_Y <- 6:7

res_dts <- dtSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list_X   = sweep_list_dts_X,
  sweep_range_Y  = sweep_range_dts_Y,
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_dts)
#> DTS-QCA Summary
#> =============== 
#> 
#> Analysis Parameters:
#>   Outcome: Y 
#>   Conditions: X1, X2, X3 
#>   Consistency cutoff: 0.8 
#>   Frequency cutoff: 1 
#> 
#> Results by Threshold:
#> 
#>  thrY combo_id             thrX     expression inclS  covS n_solutions
#>     6        1 X1=6, X2=6, X3=6          X1*X2 0.833 0.441           1
#>     7        1 X1=6, X2=6, X3=6          X1*X2 0.833 0.455           1
#>     6        2 X1=7, X2=6, X3=6          X1*X2 1.000 0.382           1
#>     7        2 X1=7, X2=6, X3=6          X1*X2 1.000 0.394           1
#>     6        3 X1=6, X2=7, X3=6      X1*X2*~X3 0.818 0.265           1
#>     7        3 X1=6, X2=7, X3=6      X1*X2*~X3 0.818 0.273           1
#>     6        4 X1=7, X2=7, X3=6          X1*X2 1.000 0.294           1
#>     7        4 X1=7, X2=7, X3=6          X1*X2 1.000 0.303           1
#>     6        5 X1=6, X2=6, X3=7             X3 0.864 0.559           1
#>     7        5 X1=6, X2=6, X3=7             X3 0.864 0.576           1
#>     6        6 X1=7, X2=6, X3=7 ~X1*X3 + X1*X2 0.931 0.794           1
#>     7        6 X1=7, X2=6, X3=7 ~X1*X3 + X1*X2 0.931 0.818           1
#>     6        7 X1=6, X2=7, X3=7             X3 0.864 0.559           1
#>     7        7 X1=6, X2=7, X3=7             X3 0.864 0.576           1
#>     6        8 X1=7, X2=7, X3=7     X3 + X1*X2 0.906 0.853           1
#>     7        8 X1=7, X2=7, X3=7     X3 + X1*X2 0.906 0.879           1

Understanding the Output

Each sweep result contains:

threshold values tested,
minimized solution expression,
solution consistency (inclS),
solution coverage (covS).

General guidance:

Consistency ≥ 0.8 is typically required for sufficiency.
Coverage indicates empirical relevance; higher is better.
A solution sensitive to small threshold changes suggests low robustness.

Handling Multiple Solutions (New in v0.2.0)

Why Multiple Solutions Matter

When QCA minimization produces multiple equivalent intermediate solutions, researchers face a methodological challenge: which solution should be reported? Traditional approaches often report only the first solution (M1), but this may miss important causal heterogeneity.

TSQCA v0.2.0 addresses this by:

Detecting when multiple solutions exist
Extracting essential prime implicants (terms common to all solutions)
Identifying selective prime implicants and unique terms

Essential vs. Selective Prime Implicants

Understanding the distinction between essential and selective prime implicants is essential for robust QCA interpretation:

Type	Definition	Interpretation
Essential prime implicants	Present in ALL solutions	Robust findings; essential causal factors
Selective prime implicants	Present in SOME but not all solutions	Context-dependent; may vary across cases
Unique terms	Present in only ONE specific solution	Solution-specific; least robust

Using `extract_mode`

The extract_mode parameter controls how solutions are extracted:

Mode: “first” (Default)

# Returns only the first solution (M1)
# Backward compatible with v0.1.x
result <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7),
  extract_mode = "first"  # Default
)

Mode: “all”

# Returns all solutions concatenated
# Useful for seeing all equivalent solutions
result_all <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7),
  extract_mode = "all"
)

# Output includes n_solutions column
head(result_all$summary)
# expression column shows: "M1: A*B + C; M2: A*B + D; M3: ..."

Mode: “essential”

# Returns essential prime implicants (terms common to all solutions)
# Best for identifying robust findings
result_essential <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7),
  extract_mode = "essential"
)

# Output includes:
# - expression: essential prime implicants
# - selective_terms: terms in some but not all solutions
# - unique_terms: solution-specific terms
# - n_solutions: number of equivalent solutions

Practical Example

Consider a scenario where QCA produces three equivalent solutions:

M1: A*B + C → Y
M2: A*B + D → Y
M3: A*B + E → Y

The analysis reveals:

Component	Terms	Interpretation
Essential (EPI)	`A*B`	Present in all three solutions; robust finding
Selective (SPI)	`C, D, E`	Each appears in one solution; context-dependent
Unique (M1)	`C`	Only in solution 1
Unique (M2)	`D`	Only in solution 2
Unique (M3)	`E`	Only in solution 3

Recommendation: Report the essential prime implicants (A*B → Y) as your main finding, and discuss the selective prime implicants as alternative pathways in your discussion section.

Generating Reports (New in v0.2.0)

Overview

The generate_report() function creates comprehensive markdown reports from your analysis results. This automates the documentation process and ensures reproducibility.

Report Formats

Full Report

Contains comprehensive analysis details:

Analysis settings (for reproducibility)
Summary table across all thresholds
Per-threshold detailed results
All solution formulas
Essential and selective prime implicants
Fit measures (consistency, coverage, PRI)
Configuration charts (New in v0.5.0)

# Generate full report
generate_report(res_ots, "my_analysis_full.md", dat = dat, format = "full")

Simple Report

Designed for journal manuscripts:

Condensed format
Essential information only
Ready for supplementary materials

# Generate simple report
generate_report(res_ots, "my_analysis_simple.md", dat = dat, format = "simple")

Using Reports Effectively

For Research Papers

Run analysis with return_details = TRUE (default in v0.2.0)
Generate a simple report for the main manuscript
Generate a full report for supplementary materials

# Complete workflow
result <- otSweep(
  dat = mydata,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

# For main text
generate_report(result, "manuscript_results.md", dat = mydata, format = "simple")

# For supplementary materials
generate_report(result, "supplementary_full.md", dat = mydata, format = "full")

Accessing Analysis Parameters

All analysis parameters are stored in result$params for reproducibility:

# View stored parameters
result$params

# Includes:
# - outcome, conditions: variable names
# - thrX, thrY: threshold values
# - incl.cut, n.cut, pri.cut: QCA parameters
# - dir.exp, include: minimization settings

Best Practices

Start Small, Then Expand

When using sweep functions, the number of QCA analyses grows quickly. A systematic approach prevents wasted computation:

Step 1: Single Value Test

# Test with a single threshold first
result <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,  # Single value
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

Step 2: Small Range

# Expand to a small range
result <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:7,  # Small range
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

Step 3: Full Analysis

# Run full analysis only after confirming it works
result <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 5:9,  # Full range
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

Computational Complexity

Understanding computational complexity helps plan your analysis:

Function	Complexity	Example	Analyses
`otSweep()`	O(n)	5 Y thresholds	5
`ctSweepS()`	O(n)	5 X thresholds	5
`ctSweepM()`	O(m^k)	3 thresholds × 3 conditions	27
`dtSweep()`	O(n × m^k)	3 Y × 3^3 X	81

Managing Large Sweeps

For dtSweep() and ctSweepM(), reduce conditions first:

# Manageable: 2 × 2 × 2 = 8 combinations
sweep_list <- list(X1 = 6:7, X2 = 6:7, X3 = 6:7)

# Caution: 5 × 5 × 5 = 125 combinations
sweep_list <- list(X1 = 5:9, X2 = 5:9, X3 = 5:9)

# Avoid: 5 × 5 × 5 × 5 × 5 = 3125 combinations!
sweep_list <- list(X1 = 5:9, X2 = 5:9, X3 = 5:9, X4 = 5:9, X5 = 5:9)

Interpreting Results

When Solutions Are Stable

If the same solution appears across multiple thresholds, your findings are robust:

thrY | expression    | inclS | covS
-----|---------------|-------|------
6    | A*B + C → Y   | 0.85  | 0.72
7    | A*B + C → Y   | 0.88  | 0.68
8    | A*B + C → Y   | 0.91  | 0.65

Interpretation: The solution A*B + C is robust across threshold variations.

When Solutions Change

If solutions vary significantly, investigate the threshold sensitivity:

thrY | expression    | inclS | covS
-----|---------------|-------|------
6    | A*B + C → Y   | 0.82  | 0.75
7    | A*B → Y       | 0.89  | 0.62
8    | B*D → Y       | 0.93  | 0.45

Interpretation: Results are threshold-sensitive. Consider reporting the most theoretically justified threshold with appropriate caveats.

Negated Outcome Analysis (New in v0.3.0)

Why Analyze Negated Outcomes?

In QCA, researchers typically analyze conditions sufficient for the presence of an outcome (Y = 1). However, understanding conditions for the absence of an outcome (~Y) can provide equally valuable insights:

What conditions lead to project failure (not success)?
What factors contribute to customer dissatisfaction (not satisfaction)?
What explains policy non-adoption (not adoption)?

Using the `~` Notation

TSQCA v0.3.0 supports the QCA package’s tilde (~) notation for negated outcomes:

# Analyze conditions for Y >= threshold (standard)
result_Y <- otSweep(
  dat = dat,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

# Analyze conditions for Y < threshold (negated)
result_negY <- otSweep(
  dat = dat,
  outcome = "~Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 6:8,
  thrX = c(X1 = 7, X2 = 7, X3 = 7)
)

Interpreting Negated Results

When using ~Y, the solution shows conditions sufficient for the absence of the outcome:

Analysis	Solution Example	Interpretation
`outcome = "Y"`	`X1*X2 + X3`	High X1 AND X2, OR high X3 → High Y
`outcome = "~Y"`	`~X1~X3 + ~X2~X3`	Low X1 AND X3, OR low X2 AND X3 → Low Y

Note: Negated conditions (~X1) in the solution mean the absence of that condition (below threshold).

All Sweep Functions Support Negation

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

# ctSweepM with negated outcome
result <- ctSweepM(
  dat = dat,
  outcome = "~Y",
  conditions = c("X1", "X2"),
  sweep_list = list(X1 = 6:7, X2 = 6:7),
  thrY = 7
)

# dtSweep with negated outcome
result <- dtSweep(
  dat = dat,
  outcome = "~Y",
  conditions = c("X1", "X2"),
  sweep_list_X = list(X1 = 6:7, X2 = 7),
  sweep_range_Y = 6:8
)

Checking Negation in Results

The params object stores whether negation was used:

result <- otSweep(dat = dat, outcome = "~Y", ...)

# Check if negated
result$params$negate_outcome
# [1] TRUE

result$params$outcome
# [1] "~Y"

Configuration Charts (New in v0.5.0)

TSQCA can generate Fiss-style configuration charts (Table 5 format) commonly used in QCA publications.

Automatic Inclusion in Reports

Configuration charts are now automatically included in reports generated by generate_report():

# Generate report with configuration charts (default)
generate_report(result, "my_report.md", dat = dat, format = "full")

# Disable charts if needed
generate_report(result, "my_report.md", dat = dat, include_chart = FALSE)

# Use LaTeX symbols for academic papers
generate_report(result, "my_report.md", dat = dat, chart_symbol_set = "latex")

Standalone Chart Functions

You can also generate configuration charts directly:

# From path strings
paths <- c("A*B*~C", "A*D", "B*E")
chart <- config_chart_from_paths(paths)
cat(chart)
#> | Condition | M1 | M2 | M3 |
#> |:--:|:--:|:--:|:--:|
#> | A | ● | ● |  |
#> | B | ● |  | ● |
#> | C | ⊗ |  |  |
#> | D |  | ● |  |
#> | E |  |  | ● |
#> 
#> *● = presence, ⊗ = absence, blank = don't care*

Symbol Sets

Three symbol sets are available:

# ASCII (maximum compatibility)
cat(config_chart_from_paths(paths, symbol_set = "ascii"))
#> | Condition | M1 | M2 | M3 |
#> |:--:|:--:|:--:|:--:|
#> | A | O | O |  |
#> | B | O |  | O |
#> | C | X |  |  |
#> | D |  | O |  |
#> | E |  |  | O |
#> 
#> *O = presence, X = absence, blank = don't care*

# LaTeX (for PDF/academic papers)
cat(config_chart_from_paths(paths, symbol_set = "latex"))
# Output: $\bullet$ for presence, $\otimes$ for absence

Multiple Solutions

When you have multiple equivalent solutions:

solutions <- list(
  c("A*B", "C*D"),
  c("A*B", "C*E")
)
chart <- config_chart_multi_solutions(solutions)
cat(chart)
#> **Note:** 2 equivalent solutions exist. Tables are shown separately below.
#> 
#> ### Solution M1
#> 
#> | Condition | M1 | M2 |
#> |:--:|:--:|:--:|
#> | A | ● |  |
#> | B | ● |  |
#> | C |  | ● |
#> | D |  | ● |
#> 
#> ---
#> 
#> ### Solution M2
#> 
#> | Condition | M1 | M2 |
#> |:--:|:--:|:--:|
#> | A | ● |  |
#> | B | ● |  |
#> | C |  | ● |
#> | E |  | ● |
#> 
#> *● = presence, ⊗ = absence, blank = don't care*

Conclusion

TSQCA provides a structured and reproducible way to evaluate
how threshold choices influence QCA results.

Using CTS, MCTS, OTS, and DTS sweeps, researchers can:

assess robustness,
identify stable causal patterns,
detect threshold-sensitive relationships,
and strengthen QCA validity.

New in v0.5.0:

Configuration charts automatically included in reports
New parameters: include_chart, chart_symbol_set
Standalone chart functions: config_chart_from_paths(), config_chart_multi_solutions()

New in v0.3.0:

QCA-compatible argument names (outcome, conditions)
Negated outcome support (~Y notation)
Backward compatibility with deprecation warnings

New in v0.2.0:

Detect and analyze multiple equivalent solutions
Extract essential prime implicants for robust findings
Generate comprehensive reports automatically
Access stored parameters for reproducibility

References

For more information on TS-QCA methodology, see:

Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. DOI: 10.7208/chicago/9780226702797.001.0001
Duşa, A. (2019). QCA with R: A Comprehensive Resource. Springer. DOI: 10.1007/978-3-319-75668-4
Oana, I.-E., & Schneider, C. Q. (2024). A Robustness Test Protocol for Applied QCA: Theory and R Software Application. Sociological Methods & Research, 53(1), 57–88. DOI: 10.1177/00491241211036158

Session Info

sessionInfo()
#> R version 4.4.2 (2024-10-31 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26200)
#> 
#> Matrix products: default
#> 
#> 
#> locale:
#> [1] LC_COLLATE=C                    LC_CTYPE=Japanese_Japan.utf8   
#> [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C                   
#> [5] LC_TIME=Japanese_Japan.utf8    
#> 
#> time zone: Asia/Tokyo
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] QCA_3.23    admisc_0.39 TSQCA_1.0.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.5         knitr_1.50        rlang_1.1.6       xfun_0.55        
#>  [5] otel_0.2.0        promises_1.5.0    shiny_1.12.1      jsonlite_2.0.0   
#>  [9] xtable_1.8-4      htmltools_0.5.9   httpuv_1.6.16     sass_0.4.10      
#> [13] lpSolve_5.6.23    rmarkdown_2.30    evaluate_1.0.4    jquerylib_0.1.4  
#> [17] fastmap_1.2.0     yaml_2.3.10       lifecycle_1.0.4   compiler_4.4.2   
#> [21] Rcpp_1.1.0        rstudioapi_0.17.1 later_1.4.4       digest_0.6.39    
#> [25] R6_2.6.1          magrittr_2.0.4    bslib_0.9.0       declared_0.25    
#> [29] tools_4.4.2       mime_0.13         venn_1.12         cachem_1.1.0

TSQCA Tutorial (English)