---
title: "TSQCA Tutorial (English)"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{TSQCA Tutorial (English)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# 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.

> **Important (v1.2.0)**: Version 1.2.0 fixed a critical bug where intermediate solutions (using `dir.exp`) were incorrectly extracted. If you used intermediate solutions in previous versions, please re-run your analyses. Reports now also display the Solution Type and include optional QCA package output for verification.

---

# Three Types of QCA Solutions (New in v1.1.0)

## Overview

QCA minimization can produce three types of solutions depending on how **logical remainders** (unobserved configurations) are handled:

| Solution Type | `include` | `dir.exp` | Logical Remainders | Description |
|--------------|-----------|-----------|-------------------|-------------|
| **Complex** (default) | `""` | `NULL` | Not used | Most conservative; only observed configurations |
| **Parsimonious** | `"?"` | `NULL` | All used | Most simplified; uses all remainders |
| **Intermediate** | `"?"` | `c(1,1,...)` | Theory-guided | Uses remainders consistent with theory |

## QCA-Compatible Defaults (v1.1.0)

As of v1.1.0, TSQCA uses the **same defaults** as `QCA::minimize()`:

- `include = ""` → Does not include logical remainders
- `dir.exp = NULL` → No directional expectations

This produces the **complex (conservative) solution** by default.

## Example: Computing All Three Solutions

```{r}
library(TSQCA)
data(sample_data)
thrX <- c(X1 = 7, X2 = 7, X3 = 7)

# 1. Complex Solution (default, 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)
)
cat("Complex Solution:", result_comp$summary$expression, "\n")

# 2. Parsimonious Solution (uses all logical remainders)
result_pars <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?"  # Include logical remainders
)
cat("Parsimonious Solution:", result_pars$summary$expression, "\n")

# 3. Intermediate Solution (theory-guided remainders)
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 to contribute positively
)
cat("Intermediate Solution:", result_int$summary$expression, "\n")
```

## When to Use Each Solution

| Solution | When to Use | Pros | Cons |
|----------|-------------|------|------|
| **Complex** | Exploratory analysis, maximum caution | No assumptions about unobserved cases | Often too complex for interpretation |
| **Parsimonious** | Robustness checks, simplicity | Most simplified form | May rely on implausible assumptions |
| **Intermediate** | Theory-driven analysis, publications | Balances parsimony and theory | Requires theoretical justification |

> **Note:** The **intermediate solution** is most commonly reported in QCA publications (Ragin, 2008; Fiss, 2011), as it incorporates theoretical knowledge while avoiding implausible simplifying assumptions.

## Migration from v1.0.0

If you were using TSQCA v1.0.0, note that the default behavior has changed:

```{r, eval=FALSE}
# v1.0.0 (intermediate solution by default due to bug)
result <- otSweep(dat, "Y", c("X1", "X2", "X3"), sweep_range = 7, thrX = thrX)

# v1.1.0: To get the same result as v1.0.0, explicitly specify:
result <- otSweep(
  dat = sample_data,
  outcome = "Y",
  conditions = c("X1", "X2", "X3"),
  sweep_range = 7,
  thrX = thrX,
  include = "?",
  dir.exp = c(1, 1, 1)  # Explicit intermediate solution
)
```

---

# 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:
```{r}
library(TSQCA)
data("sample_data")
dat <- sample_data
str(dat)
```

Define outcome and conditions:
```{r}
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

1. **Do NOT sweep binary variables** — they are already binarized (0/1)
2. **Use threshold = 1 for binary variables** to preserve their original values
3. **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()`:

```{r, eval=FALSE}
# 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
)

# Using intermediate solution (most common in publications)
res_mixed <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  include        = "?",           # Include logical remainders
  dir.exp        = c(1, 1, 1)     # Directional expectations (intermediate)
)
```

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

### Common Mistake

```{r, eval=FALSE}
# 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:

```{r, eval=FALSE}
# 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), ")")
  }
})
```

## Using Pre-Calibrated Fuzzy Conditions (`pre_calibrated`)

Version 1.3.0 introduces the `pre_calibrated` argument to all four sweep
functions (`otSweep`, `ctSweepS`, `ctSweepM`, `dtSweep`). This argument
enables a **mixed crisp/fuzzy** workflow: some conditions can be passed as
fuzzy-set membership scores (values in [0, 1] produced by
`QCA::calibrate()`), while other conditions are swept on their raw scale
as usual.

### How it works

- Variables listed in `pre_calibrated` are passed directly to
  `QCA::truthTable()` **without binarization**.
- All other conditions are binarized using `qca_bin()` at the thresholds
  specified in `thrX`.
- Pre-calibrated variables do not require a `thrX` entry, since no
  binarization is applied to them.

### Validation

The function raises an error if:

- A variable listed in `pre_calibrated` is not found in `conditions`.
- Its values fall outside the [0, 1] range (indicating that a raw-scale
  variable was passed by mistake).

A warning is issued if the variable contains `NA` values.

### Sweep variables vs. pre-calibrated variables

Sweep variables should be kept on their **raw (original) scale**. When you
sweep a variable on its raw scale (e.g., Likert scores 6, 7, 8), each
threshold carries direct substantive meaning. Pre-calibrating a sweep
variable into fuzzy values and then sweeping over those values (e.g., 0.3,
0.5, 0.7) makes interpretation indirect and harder to justify.

If a variable appears in both `pre_calibrated` and a sweep list, TSQCA
issues a warning and uses the pre-calibrated values, ignoring the sweep
thresholds.

### Recommended practice

| Variable type | Recommended approach |
|---|---|
| Conditions you want to sweep | Keep on raw scale; use `qca_bin()` via `thrX` |
| Conditions with theoretically grounded fuzzy calibration | Pre-calibrate and list in `pre_calibrated` |
| Binary conditions (0/1) | Use `thrX = 1`; optionally list in `pre_calibrated` |

*A worked example with sample data will be added in a future release.*

---

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

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

## Default: Complex Solution
```{r, error=TRUE}
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)

# Default: Complex solution (include = "", dir.exp = NULL)
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,
  return_details = TRUE
)

summary(res_cts)
```

## Intermediate Solution (Theory-Driven)
```{r, error=TRUE}
# Intermediate solution: specify include = "?" and dir.exp
res_cts_int <- ctSweepS(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_var      = sweep_var,
  sweep_range    = sweep_range,
  thrY           = thrY,
  thrX_default   = thrX_default,
  include        = "?",           # Include logical remainders
  dir.exp        = c(1, 1, 1),    # Directional expectations
  return_details = TRUE
)

summary(res_cts_int)
```

---

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

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

## Default: Complex Solution
```{r, error=TRUE}
# Create a sweep list specifying thresholds for each condition
sweep_list <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

# Default: Complex solution
res_mcts <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  return_details = TRUE
)

summary(res_mcts)
```

## Intermediate Solution (Theory-Driven)
```{r, error=TRUE}
# Intermediate solution: specify include = "?" and dir.exp
res_mcts_int <- ctSweepM(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list     = sweep_list,
  thrY           = 7,
  include        = "?",
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_mcts_int)
```

---

# OTS–QCA: Outcome Sweep (`otSweep`)

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

## Default: Complex Solution
```{r}
# Default: Complex solution
res_ots <- otSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_range    = 6:8,
  thrX           = c(X1 = 7, X2 = 7, X3 = 7),
  return_details = TRUE
)

summary(res_ots)
```

## Intermediate Solution (Theory-Driven)
```{r}
# Intermediate solution: specify include = "?" and dir.exp
res_ots_int <- otSweep(
  dat            = dat,
  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
)

summary(res_ots_int)
```

---

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

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

## Default: Complex Solution
```{r}
sweep_list_dts_X <- list(
  X1 = 6:7,
  X2 = 6:7,
  X3 = 6:7
)

sweep_range_dts_Y <- 6:7

# Default: Complex solution
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,
  return_details = TRUE
)

summary(res_dts)
```

## Intermediate Solution (Theory-Driven)
```{r}
# Intermediate solution: specify include = "?" and dir.exp
res_dts_int <- dtSweep(
  dat            = dat,
  outcome        = "Y",
  conditions     = c("X1", "X2", "X3"),
  sweep_list_X   = sweep_list_dts_X,
  sweep_range_Y  = sweep_range_dts_Y,
  include        = "?",
  dir.exp        = c(1, 1, 1),
  return_details = TRUE
)

summary(res_dts_int)
```

---

# 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:

1. **Detecting** when multiple solutions exist
2. **Extracting** essential prime implicants (terms common to all solutions)
3. **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)
```{r, eval=FALSE}
# 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"
```{r, eval=FALSE}
# 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"
```{r, eval=FALSE}
# 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)

```{r, eval=FALSE}
# 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

```{r, eval=FALSE}
# Generate simple report
generate_report(res_ots, "my_analysis_simple.md", dat = dat, format = "simple")
```

## Using Reports Effectively

### For Research Papers

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

```{r, eval=FALSE}
# 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:

```{r, eval=FALSE}
# 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
```{r, eval=FALSE}
# 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
```{r, eval=FALSE}
# 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
```{r, eval=FALSE}
# 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:

```{r, eval=FALSE}
# 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:

```{r, eval=FALSE}
# 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

```{r, eval=FALSE}
# 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:

```{r, eval=FALSE}
result <- otSweep(dat = dat, outcome = "~Y", ...)

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

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

---

# Fiss (2011) Core/Peripheral Classification (New in v1.3.2)

## What Is the Core/Peripheral Distinction?

Fiss (2011) introduced a critical refinement to QCA configuration tables
that is now standard in the *Academy of Management Journal* and other top
management journals. Beyond simply indicating whether a condition is present
or absent in a solution, Fiss distinguishes between:

| Type | Definition | Symbol |
|------|------------|--------|
| **Core condition** | Appears in BOTH the parsimonious AND intermediate solutions | ● (large) |
| **Peripheral condition** | Appears in the intermediate solution ONLY | ⊙ (small) |

The intuition is straightforward: conditions that survive even the most
aggressive simplification (parsimonious solution) are more central to the
causal story than conditions that only appear when theoretical constraints
are applied (intermediate solution).

> **Why this matters:** Prior to v1.3.2, TSQCA configuration charts treated
> all conditions as equivalent, using only two symbols. This is technically
> incorrect per Fiss (2011) and may raise concerns from reviewers at
> journals that require Fiss notation.

## The Four-Symbol Set

```
● = core condition present      (large filled circle)
⊗ = core condition absent       (large circle-times)
⊙ = peripheral condition present (small circled dot)
⊘ = peripheral condition absent  (small circled slash)
  = don't care (blank)
```

For LaTeX / academic PDF output: `$\bullet$`, `$\otimes$`, `$\odot$`, `$\oslash$`.

## Workflow

`compute_fiss_core()` requires three conditions from the original sweep:

1. `include = "?"` — logical remainders must be enabled
2. `dir.exp` specified — intermediate solution must have been requested
3. `return_details = TRUE` — truth tables must be stored

```{r, eval=FALSE}
library(TSQCA)
data(sample_data)

# Step 1: Run intermediate sweep (all three conditions above are met)
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    = "?",           # required
  dir.exp    = c(1, 1, 1),   # required
  return_details = TRUE       # required
)

# Step 2: Compute Fiss core/peripheral classification
# For each threshold, compute_fiss_core() re-runs QCA::minimize()
# with dir.exp = NULL to obtain the parsimonious solution,
# then compares it to the stored intermediate solution.
res_fiss <- compute_fiss_core(res, conditions = c("X1", "X2", "X3"))

# Step 3: Inspect a specific threshold
print_fiss_summary(res_fiss, thr_key = "7")

# Step 4: Generate Fiss-style four-symbol chart
cat(generate_fiss_chart(res_fiss, symbol_set = "unicode"))

# For academic papers (LaTeX)
cat(generate_fiss_chart(res_fiss, symbol_set = "latex"))

# Step 5: Include in report
generate_report(
  res_fiss,
  output_file      = "fiss_report.md",
  format           = "full",
  include_fiss_core = TRUE,          # activates four-symbol charts
  chart_symbol_set  = "unicode"
)
```

## Reading the Output

`print_fiss_summary()` shows the classification term-by-term:

```
=== Fiss Core/Peripheral Classification (thrY = 7) ===
Parsimonious : X1*X2
Intermediate : X3 + X1*X2

[Term M1] X3
  Periph. present : X3

[Term M2] X1*X2
  Core present    : X1, X2
```

**Interpretation:** X1 and X2 are core (they appear in the parsimonious
solution `X1*X2`). X3 is peripheral — it only appears when theory-guided
directional expectations are applied (intermediate solution), suggesting
it plays a supporting rather than essential causal role.

## What If All Conditions Are Core?

When the parsimonious and intermediate solutions are identical, all
conditions are core and no conditions are peripheral. This indicates
maximum theoretical parsimony.

## Reference

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. doi:10.5465/amj.2011.60263120

---

# 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()`:

```{r eval=FALSE}
# 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")

# Include QCA package raw output for verification (default is TRUE)
generate_report(result, "my_report.md", dat = dat, include_raw_output = TRUE)

# Disable raw output if not needed
generate_report(result, "my_report.md", dat = dat, include_raw_output = FALSE)
```

**Note (v1.2.0)**: Reports now display the **Solution Type** (Complex/Parsimonious/Intermediate) in the Analysis Overview section, and optionally include raw QCA package output for verification purposes.

## Standalone Chart Functions

You can also generate configuration charts directly:

```{r}
# From path strings
paths <- c("A*B*~C", "A*D", "B*E")
chart <- config_chart_from_paths(paths)
cat(chart)
```

### Symbol Sets

Three symbol sets are available:

```{r}
# ASCII (maximum compatibility)
cat(config_chart_from_paths(paths, symbol_set = "ascii"))
```

```{r eval=FALSE}
# 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:

```{r}
solutions <- list(
  c("A*B", "C*D"),
  c("A*B", "C*E")
)
chart <- config_chart_multi_solutions(solutions)
cat(chart)
```

---

# 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 v1.3.2:**

- `compute_fiss_core()`: Augments sweep results with Fiss (2011) core/peripheral classification
- `generate_fiss_chart()`: Four-symbol configuration charts (●/⊗/⊙/⊘) for academic publications
- `print_fiss_summary()`: Human-readable core/peripheral breakdown per threshold
- `generate_report(..., include_fiss_core = TRUE)`: Reports with full Fiss notation

**New in v1.3.0:**

- `pre_calibrated` argument: mixed crisp/fuzzy workflow for all four sweep functions

**New in v1.1.0:**

- QCA-compatible defaults: `include = ""` and `dir.exp = NULL` (complex solution)
- Clear documentation of three solution types (complex, parsimonious, intermediate)
- Fixed critical bug in `dir.exp` handling

**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

---

# Verification for Academic Publications

**For academic publications**, we strongly recommend verifying TSQCA results directly with the QCA package before submission. This ensures accuracy and reproducibility.

## Recommended Verification Workflow

```{r eval=FALSE}
library(QCA)
library(TSQCA)

# Step 1: Run TSQCA analysis
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,
  include = "?",
  dir.exp = c(1, 1, 1),
  return_details = TRUE
)

# Step 2: Generate report with QCA output for verification
generate_report(result, "my_analysis.md", dat = sample_data, 
                include_raw_output = TRUE)  # Default is TRUE

# Step 3: Verify key results directly with QCA package
# For each important threshold, run QCA::minimize() directly
dat_bin <- sample_data
dat_bin$Y_bin <- ifelse(sample_data$Y >= 7, 1, 0)
dat_bin$X1_bin <- ifelse(sample_data$X1 >= 7, 1, 0)
dat_bin$X2_bin <- ifelse(sample_data$X2 >= 7, 1, 0)
dat_bin$X3_bin <- ifelse(sample_data$X3 >= 7, 1, 0)

tt <- truthTable(dat_bin, outcome = "Y_bin", 
                 conditions = c("X1_bin", "X2_bin", "X3_bin"),
                 incl.cut = 0.8)
sol <- minimize(tt, include = "?", dir.exp = c(1, 1, 1))
print(sol)  # Compare with TSQCA report
```

## Why Verification Matters

1. **Accuracy**: Confirms TSQCA extraction matches QCA package output
2. **Reproducibility**: Provides independent verification for reviewers
3. **Transparency**: Documents the analysis pipeline clearly

## What to Check

- Solution expressions match exactly
- Consistency and coverage values match
- Number of solutions (M1, M2, ...) is correct

> **Tip**: The "QCA Package Output" section in TSQCA reports (enabled by `include_raw_output = TRUE`) shows the direct `print(sol)` output for easy comparison.


## 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](https://doi.org/10.7208/chicago/9780226702797.001.0001)
- Duşa, A. (2019). *QCA with R: A Comprehensive Resource*. Springer. DOI: [10.1007/978-3-319-75668-4](https://doi.org/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](https://doi.org/10.1177/00491241211036158)



# Session Info
```{r}
sessionInfo()
```
