# Generating Designs

Once you have a set of profiles and (optionally) priors, you can generate a choice-based conjoint (CBC) survey design using the `cbc_design()` function. This article covers all the design methods available, their features, and how to customize designs for specific research needs.

Before starting, let’s define some basic profiles and priors to work with:

    library(cbcTools)

    profiles <- cbc_profiles(
      price = c(1, 1.5, 2, 2.5, 3),
      type = c('Fuji', 'Gala', 'Honeycrisp'),
      freshness = c('Poor', 'Average', 'Excellent')
    )

    priors <- cbc_priors(
      profiles = profiles,
      price = -0.25,
      type = c('Gala' = 0.5, 'Honeycrisp' = 1.0),
      freshness = c('Average' = 0.6, 'Excellent' = 1.2)
    )

# Design Basics

The `cbc_design()` function generates a data frame with an encoded experiment design formatted as one row per alternative. Choice questions are defined by sets of rows with the same `obsID`. Let’s start with a simple example (a random design):

    design <- cbc_design(
      profiles = profiles,
      n_alts = 2, # Alternatives per question
      n_q = 6, # Questions per respondent
      n_resp = 100 # Number of respondents
    )

    design
    #> Design method: random
    #> Structure: 100 respondents × 6 questions × 2 alternatives
    #> Profile usage: 45/45 (100.0%)
    #>
    #> 💡 Use cbc_inspect() for a more detailed summary
    #>
    #> First few rows of design:
    #>   profileID respID qID altID obsID price typeGala typeHoneycrisp
    #> 1        31      1   1     1     1   1.0        0              0
    #> 2        15      1   1     2     1   3.0        0              1
    #> 3        14      1   2     1     2   2.5        0              1
    #> 4         3      1   2     2     2   2.0        0              0
    #> 5        42      1   3     1     3   1.5        0              1
    #> 6        43      1   3     2     3   2.0        0              1
    #>   freshnessAverage freshnessExcellent
    #> 1                0                  1
    #> 2                0                  0
    #> 3                0                  0
    #> 4                0                  0
    #> 5                0                  1
    #> 6                0                  1
    #> ... and 1194 more rows

## Understanding the Design Structure

The design data frame contains several types of columns that help organize the experiment:

### ID Columns

These columns identify the structure of your experiment:

- `profileID`: Unique identifier for each profile (combination of attribute levels), that corresponds to the IDs in `profiles`
- `respID`: Respondent ID (1 to `n_resp`)
- `qID`: Question number within each respondent (1 to `n_q`)
- `altID`: Alternative number within each question (1 to `n_alts`)
- `obsID`: Unique identifier for each choice question across all respondents

### Attribute Columns

The remaining columns represent your experimental attributes. By default, categorical attributes are dummy-coded. In dummy coding, continuous attributes (like `price`) appear as-is, but categorical attributes (like `type` and `freshness`) are split into multiple binary columns.

For example, for `type`, we have the following columns:

- `typeGala` = 1 if type is “Gala”, 0 otherwise
- `typeHoneycrisp` = 1 if type is “Honeycrisp”, 0 otherwise

Here the reference level (“Fuji”) is represented when both dummy variables equal 0.

### Converting to Categorical Format

If you prefer to see categorical variables in their original format, use `cbc_decode()`:

    design_decoded <- cbc_decode(design)
    design_decoded
    #> Design method: random
    #> Structure: 100 respondents × 6 questions × 2 alternatives
    #> Profile usage: 45/45 (100.0%)
    #>
    #> 💡 Use cbc_inspect() for a more detailed summary
    #>
    #> First few rows of design:
    #>   profileID respID qID altID obsID price       type freshness
    #> 1        31      1   1     1     1   1.0       Fuji Excellent
    #> 2        15      1   1     2     1   3.0 Honeycrisp      Poor
    #> 3        14      1   2     1     2   2.5 Honeycrisp      Poor
    #> 4         3      1   2     2     2   2.0       Fuji      Poor
    #> 5        42      1   3     1     3   1.5 Honeycrisp Excellent
    #> 6        43      1   3     2     3   2.0 Honeycrisp Excellent
    #> ... and 1194 more rows

The decoded version shows:

- `type` as a categorical variable with levels “Fuji”, “Gala”, “Honeycrisp”
- `freshness` as a categorical variable with levels “Poor”, “Average”, “Excellent”
- `price` remains unchanged (continuous variables don’t need decoding)

Both forms of the design (dummy-coded and categorical) are convenient for different purposes, though they are otherwise equivalent.

# Design Methods

The `cbc_design()` function supports several design generation methods, each with different strengths and use cases:

## Method Comparison Table

Method | Speed | Efficiency | No Choice | Labeled | Restrictions | Balanced Sampling | Blocking | Interactions
--- | --- | --- | --- | --- | --- | --- | --- | ---
`"random"` | Fast | Low | ✓ | ✓ | ✓ | ✓ | ✗ | ✓
`"shortcut"` | Fast | Medium | ✓ | ✓ | ✓ | ✓ | ✗ | ✗
`"minoverlap"` | Fast | Medium | ✓ | ✓ | ✓ | ✓ | ✗ | ✗
`"balanced"` | Fast | Medium | ✓ | ✓ | ✓ | ✓ | ✗ | ✗
`"stochastic"` | Slow | High | ✓ | ✓ | ✓ | ✗ | ✓ | ✓
`"modfed"` | Medium | High | ✓ | ✓ | ✓ | ✗ | ✓ | ✓
`"cea"` | Medium | High | ✓ | ✓ | ✗ | ✗ | ✓ | ✓

All design methods ensure:

1. No duplicate profiles within any choice set
2. No duplicate choice sets within any respondent
3. Dominance removal (if enabled) eliminates choice sets with dominant alternatives (requires priors)

## `"random"` Method

The `"random"` method is the default and creates designs by randomly sampling profiles for each respondent independently. This ensures maximum diversity but may be less statistically efficient.

    design_random <- cbc_design(
      profiles = profiles,
      method = "random",
      n_alts = 2,
      n_q = 6,
      n_resp = 100
    )

    # Quick inspection
    cbc_inspect(design_random, sections = "structure")
    #> DESIGN SUMMARY
    #> =========================
    #>
    #> STRUCTURE
    #> ================
    #> Method: random
    #> Created: 2025-09-23 12:45:07
    #> Respondents: 100
    #> Questions per respondent: 6
    #> Alternatives per question: 2
    #> Total choice sets: 600
    #> Profile usage: 45/45 (100.0%)

When to use:

- Large sample sizes where efficiency matters less
- Want maximum diversity across respondents
- No strong prior assumptions about parameters
- Uncertain whether interactions might be important
- Quick prototyping or testing

## Frequency-Based Methods

The `"shortcut"`, `"minoverlap"`, and `"balanced"` methods use greedy algorithms to balance attribute level frequencies and minimize overlap. While they prioritize different metrics, they often can result in similar solutions. Each method has a different objective:

- The `"shortcut"` method balances attribute level frequencies while avoiding duplicate profiles within questions.
- The `"minoverlap"` method prioritizes minimizing attribute overlap within choice questions.
- The `"balanced"` method optimizes both frequency balance and pairwise attribute interactions.

    design_shortcut <- cbc_design(
      profiles = profiles,
      method = "shortcut",
      n_alts = 2,
      n_q = 6,
      n_resp = 100
    )

    design_minoverlap <- cbc_design(
      profiles = profiles,
      method = "minoverlap",
      n_alts = 2,
      n_q = 6,
      n_resp = 100
    )

    design_balanced <- cbc_design(
      profiles = profiles,
      method = "balanced",
      n_alts = 2,
      n_q = 6,
      n_resp = 100
    )

## D-Optimal Methods

These methods minimize D-error to create statistically efficient designs. They require more computation but produce higher-quality designs, especially with good priors.

Unlike the previous methods, these methods identify a single d-optimal design and then repeat that design across each respondent. In contrast, the other methods create a unique design for each respondent.

Each method has a different approach:

- The `"stochastic"` method uses random profile swapping to minimize the d-error, accepting the first improvement found. This is a faster algorithm as a compromise between speed and exhaustiveness.
- The `"modfed"` (Modified Fedorov) method exhaustively tests all possible profile swaps for each position. It is slower than other methods though more thorough.
- The `"cea"` (Coordinate Exchange Algorithm) method optimizes attribute-by-attribute, testing all possible levels for each attribute. It is faster than `"modfed"`, though requires all possible profiles and cannot accept restricted profile sets.

For the `"modfed"` and `"cea"` methods, designs are by default created using the much faster algorithms in the {idefix} package. You can use the (slower) {cbcTools} versions of these methods by setting `use_idefix = FALSE`.

For the examples below, we have `n_start = 1`, meaning it will only run one design search (which is faster), but you may want to run a longer search by increasing `n_start`. The best design across all starts is chosen.

    design_stochastic <- cbc_design(
      profiles = profiles,
      method = "stochastic",
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      priors = priors,
      n_start = 1 # Number of random starting points
    )

    design_modfed <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      priors = priors,
      method = "modfed",
      n_start = 1
    )

    design_cea <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      priors = priors,
      method = "cea",
      n_start = 1
    )

Notice also that in the examples above we provided the `priors` to each design. This will optimize the design around these assumed priors by minimizing the \(D_p\)-error. If you are uncertain what the true parameters are, you can omit the `priors` argument and the algorithms will minimize the \(D_0\)-error. See the Computing D-error page for more details on how these errors are computed.

# Comparing Designs

You can compare the results of different designs using the `cbc_compare()` function. This provides a comprehensive overview of differences in structure as well as common metrics such as D-error, overlap, and balance.

    cbc_compare(
      "Random" = design_random,
      "Shortcut" = design_shortcut,
      "Min Overlap" = design_minoverlap,
      "Balanced" = design_balanced,
      "Stochastic" = design_stochastic,
      "Modfed" = design_modfed,
      "CEA" = design_cea
    )
    #> CBC Design Comparison
    #> =====================
    #> Designs compared: 7
    #> Metrics: structure, efficiency, balance, overlap
    #> Sorted by: d_error (ascending)
    #>
    #> Structure
    #> =====================
    #>       Design     Method respondents questions
    #>       Modfed     modfed         100         6
    #>          CEA        cea         100         6
    #>   Stochastic stochastic         100         6
    #>       Random     random         100         6
    #>     Shortcut   shortcut         100         6
    #>  Min Overlap minoverlap         100         6
    #>     Balanced   balanced         100         6
    #>  Alternatives Blocks Profile Usage
    #>             2      1 (10/45) 22.2%
    #>             2      1 (11/45) 24.4%
    #>             2      1 (12/45) 26.7%
    #>             2      1  (45/45) 100%
    #>             2      1  (45/45) 100%
    #>             2      1  (45/45) 100%
    #>             2      1  (45/45) 100%
    #>  No Choice Labeled?
    #>         No       No
    #>         No       No
    #>         No       No
    #>         No       No
    #>         No       No
    #>         No       No
    #>         No       No
    #>
    #> Design Metrics
    #> =====================
    #>       Design     Method D-Error (Null) D-Error (Prior) Balance Overlap
    #>       Modfed     modfed       0.850283        1.023292   0.744   0.267
    #>          CEA        cea       0.858174        1.043690   0.777   0.233
    #>   Stochastic stochastic       1.203841        1.485359   0.686   0.300
    #>       Random     random             NA              NA   0.739   0.477
    #>     Shortcut   shortcut             NA              NA   0.740   0.267
    #>  Min Overlap minoverlap             NA              NA   0.734   0.267
    #>     Balanced   balanced             NA              NA   0.740   0.265
    #>
    #> Interpretation:
    #> - D-Error: Lower is better (design efficiency)
    #> - Balance: Higher is better (level distribution)
    #> - Overlap: Lower is better (attribute variation)
    #> - Profile Usage: Higher means more profiles used
    #>
    #> Best performers:
    #> - D-Error: Modfed (1.023292)
    #> - Balance: CEA (0.777)
    #> - Overlap: CEA (0.233)
    #> - Profile Usage: Random (100.0%)
    #>
    #> Use summary() for detailed information on any one design.

# Design Features

## No-Choice Option

Add a “no-choice” alternative to allow respondents to opt out by including the argument `no_choice = TRUE`. If you are using priors in your design (optional), then you must also provide a `no_choice` value in your priors:

    # For D-optimal methods, must include no_choice in priors
    priors_nochoice <- cbc_priors(
      profiles = profiles,
      price = -0.1,
      type = c(0.1, 0.2),
      freshness = c(0.1, 0.2),
      no_choice = -0.5 # Negative value makes no-choice less attractive
    )

    design_nochoice <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      no_choice = TRUE,
      priors = priors_nochoice,
      method = "stochastic"
    )

    head(design_nochoice)
    #> Design method: stochastic
    #> Structure: 100 respondents × 6 questions × 2 alternatives
    #> Profile usage: 9/45 (20.0%)
    #> D-error: 0.823065
    #>
    #> 💡 Use cbc_inspect() for a more detailed summary
    #>
    #> First few rows of design:
    #>   profileID blockID respID qID altID obsID price typeGala typeHoneycrisp
    #> 1        15       1      1   1     1     1   3.0        0              1
    #> 2        24       1      1   1     2     1   2.5        1              0
    #> 3         0       1      1   1     3     1   0.0        0              0
    #> 4         7       1      1   2     1     2   1.5        1              0
    #> 5        34       1      1   2     2     2   2.5        0              0
    #> 6         0       1      1   2     3     2   0.0        0              0
    #>   freshnessAverage freshnessExcellent no_choice
    #> 1                0                  0         0
    #> 2                1                  0         0
    #> 3                0                  0         1
    #> 4                0                  0         0
    #> 5                0                  1         0
    #> 6                0                  0         1

> Note: Designs with no-choice options must be dummy-coded and cannot be converted back to categorical format.

## Labeled Designs

Create “labeled” or “alternative-specific” designs where one attribute serves as a label using the `label` argument:

    design_labeled <- cbc_design(
      profiles = profiles,
      n_alts = 3, # Will be overridden to match number of type levels
      n_q = 6,
      n_resp = 100,
      label = "type", # Use 'type' attribute as labels
      method = "random"
    )

    head(design_labeled)
    #> Design method: random
    #> Structure: 100 respondents × 6 questions × 3 alternatives
    #> Profile usage: 45/45 (100.0%)
    #>
    #> 💡 Use cbc_inspect() for a more detailed summary
    #>
    #> First few rows of design:
    #>   profileID respID qID altID obsID price typeGala typeHoneycrisp
    #> 1        20      1   1     1     1   3.0        0              0
    #> 2        21      1   1     2     1   1.0        1              0
    #> 3        14      1   1     3     1   2.5        0              1
    #> 4         5      1   2     1     2   3.0        0              0
    #> 5        38      1   2     2     2   2.0        1              0
    #> 6        44      1   2     3     2   2.5        0              1
    #>   freshnessAverage freshnessExcellent
    #> 1                1                  0
    #> 2                1                  0
    #> 3                0                  0
    #> 4                0                  0
    #> 5                0                  1
    #> 6                0                  1

## Balanced Sampling

When working with attribute-specific features (attributes that only apply to certain levels of another attribute), you may encounter balance problems. For example, electric vehicles have an all-electric driving range attribute, but gas and hybrid vehicles do not. Using restrictions to handle this can lead to over-representation of certain attribute levels.

The `balance_by` argument solves this by ensuring balanced sampling across specified attributes. Here’s an example with vehicle powertrains and electric range:

    # Create profiles with attribute-specific feature
    profiles_vehicles <- cbc_profiles(
      price = c(15, 20, 25), # Price in $1,000s
      fuelEconomy = c(20, 25, 30), # Fuel economy (mpg)
      powertrain = c('gas', 'hybrid', 'electric'),
      range_electric = c(0, 100, 150, 200, 250) # EV driving range (miles)
    ) |>
      # Restrict non-electric powertrains from having range_electric other than 0
      cbc_restrict(
        (powertrain == 'electric') & (range_electric == 0),
        (powertrain != 'electric') & (range_electric != 0)
      )

    # Check the balance problem - electric vehicles are over-represented
    table(profiles_vehicles$powertrain)
    #>
    #>      gas   hybrid electric
    #>        9        9       36

Without `balance_by`, the restricted profiles lead to unbalanced designs:

    # Design without balance_by - electric powertrains over-represented
    design_unbalanced <- cbc_design(
      profiles = profiles_vehicles,
      n_alts = 3,
      n_q = 8,
      n_resp = 100,
      method = "random"
    ) |>
        cbc_decode() # Converts dummy-coded powertrain attributes back to "powertrain"

    # Check powertrain distribution - again, electric is over-represented
    table(design_unbalanced$powertrain)
    #>
    #>      gas   hybrid electric
    #>      389      382     1629

Using `balance_by` ensures balanced sampling across powertrains:

    # Design with balance_by - balanced powertrain representation
    design_balanced <- cbc_design(
      profiles = profiles_vehicles,
      n_alts = 3,
      n_q = 8,
      n_resp = 100,
      method = "random",
      balance_by = "powertrain" # Balance across powertrain levels
    ) |>
        cbc_decode()

    # Check improved powertrain distribution - now powertrain is balanced
    table(design_balanced$powertrain)
    #>
    #>      gas   hybrid electric
    #>      777      798      825

You can also balance across multiple attributes simultaneously:

    # Balance across combinations of powertrain and price
    design_multi_balance <- cbc_design(
      profiles = profiles_vehicles,
      n_alts = 3,
      n_q = 8,
      n_resp = 50,
      method = "random",
      balance_by = c("powertrain", "price")
    ) |>
        cbc_decode()

    # Check improved powertrain distribution - now powertrain and price are balanced
    table(design_multi_balance$powertrain)
    #>
    #>      gas   hybrid electric
    #>      407      422      371
    table(design_multi_balance$price)
    #>
    #>  15  20  25
    #> 381 419 400

> Note: The `balance_by` argument cannot be used simultaneously with `label`. Choose one approach based on your design needs.

> Note: The `balance_by` feature is only available for `"random"`, `"shortcut"`, `"minoverlap"`, and `"balanced"` methods. D-optimal methods (`"stochastic"`, `"modfed"`, `"cea"`) prioritize statistical efficiency over balance and do not support this feature.

## Blocking

For D-optimal methods, create multiple design blocks to reduce respondent burden using the `n_blocks` argument. In the example below, two blocks are created with each block containing `n_q = 6` questions:

    design_blocked <- cbc_design(
      profiles = profiles,
      method = "stochastic",
      priors = priors,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      n_blocks = 2 # Create 2 different design blocks
    )

    # Check block allocation
    table(design_blocked$blockID)
    #>
    #>   1   2
    #> 600 600

The way blocking works is that a single design is created with `n_q*n_blocks` questions, then those questions are allocated into blocks with `n_q` questions per block. For the `"modfed"` and `"cea"` methods, the blocking is handled using the internal logic of the {idefix} package, which allocates questions to the blocks in a balanced way. You can use the (slower) {cbcTools} versions of these methods by setting `use_idefix = FALSE`. For the `"stochastic"` method, questions are randomly allocated to blocks.

## Dominance Removal

Remove choice sets where one alternative dominates others based on parameter preferences. There are two forms of dominance removal:

1. Total dominance: Occurs when one alternative has such a high predicted choice probability (based on the prior coefficients) that it would be chosen by virtually all respondents. This creates choice sets with little information value since the outcome is predetermined. The `dominance_threshold` parameter controls this - alternatives with choice probabilities above this threshold (e.g., 0.8 = 80%) are considered dominant.
2. Partial dominance: Occurs when one alternative is superior to all others across every individual attribute component of the utility function (again, based on prior coefficients). For example, if Alternative A has higher partial utilities than Alternative B for every single attribute (price, type, freshness), then A partially dominates B regardless of the overall choice probability. This type of dominance is detected by comparing the attribute-level contributions to utility.

Both forms of dominance create unrealistic choice scenarios that provide less information about respondent preferences, so removing them generally improves design quality.

    design_no_dominance <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      priors = priors,
      method = "stochastic",
      remove_dominant = TRUE,
      dominance_types = c("total", "partial"),
      dominance_threshold = 0.8
    )

## Interactions

Include interaction effects in D-optimal designs by specifying them in your prior model. Interactions capture how the effect of one attribute depends on the level of another attribute. The design optimization then accounts for these interaction terms when minimizing D-error.

Interactions are specified via the priors defined by `cbc_priors()`. For example:

    # Create priors with interactions
    priors_interactions <- cbc_priors(
      profiles = profiles,
      price = -0.25,
      type = c("Fuji" = 0.5, "Gala" = 1.0),
      freshness = c(0.6, 1.2),
      interactions = list(
        # Price is less negative (less price sensitive) for Fuji apples
        int_spec(
          between = c("price", "type"),
          with_level = "Fuji",
          value = 0.5
        ),
        # Price is slightly less negative for Gala apples
        int_spec(
          between = c("price", "type"),
          with_level = "Gala",
          value = 0.2
        )
        # Honeycrisp uses reference level (no additional interaction term)
      )
    )

    design_interactions <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 6,
      n_resp = 100,
      priors = priors_interactions,
      method = "stochastic"
    )

When you include interactions in the prior model, the design optimization:

1. Accounts for interaction parameters when computing choice probabilities
2. Optimizes profile combinations that provide information about both main effects AND interactions
3. Creates choice sets that help distinguish between different interaction effects

This leads to more efficient designs when interaction effects truly exist in your population, but can reduce efficiency for estimating main effects if interactions are misspecified or don’t actually exist.

See the Specifying Priors article for more details and options on defining priors with interactions.

# Comprehensive Design Inspection

Use `cbc_inspect()` for detailed design analysis:

    # Detailed inspection of the stochastic design
    cbc_inspect(
      design_stochastic,
      sections = "all"
    )
    #> DESIGN SUMMARY
    #> =========================
    #>
    #> STRUCTURE
    #> ================
    #> Method: stochastic
    #> Created: 2025-09-23 12:45:07
    #> Respondents: 100
    #> Questions per respondent: 6
    #> Alternatives per question: 2
    #> Total choice sets: 600
    #> Profile usage: 12/45 (26.7%)
    #>
    #> SUMMARY METRICS
    #> =================
    #> D-error (with priors): 1.485359
    #> D-error (null model): 1.203841
    #> (Lower values indicate more efficient designs)
    #>
    #> Overall balance score: 0.686 (higher is better)
    #> Overall overlap score: 0.300 (lower is better)
    #>
    #> VARIABLE ENCODING
    #> =================
    #> Format: Dummy-coded (type, freshness)
    #> 💡 Use cbc_decode_design() to convert to categorical format
    #>
    #> ATTRIBUTE BALANCE
    #> =================
    #> Overall balance score: 0.686 (higher is better)
    #>
    #> Individual attribute level counts:
    #>
    #> price:
    #>
    #>   1 1.5   2 2.5   3
    #> 300 200 200 400 100
    #>   Balance score: 0.678 (higher is better)
    #>
    #> typeGala:
    #>
    #>   0   1
    #> 800 400
    #>   Balance score: 0.680 (higher is better)
    #>
    #> typeHoneycrisp:
    #>
    #>   0   1
    #> 900 300
    #>   Balance score: 0.586 (higher is better)
    #>
    #> freshnessAverage:
    #>
    #>   0   1
    #> 700 500
    #>   Balance score: 0.809 (higher is better)
    #>
    #> freshnessExcellent:
    #>
    #>   0   1
    #> 800 400
    #>   Balance score: 0.680 (higher is better)
    #>
    #> ATTRIBUTE OVERLAP
    #> =================
    #> Overall overlap score: 0.300 (lower is better)
    #>
    #> Counts of attribute overlap:
    #> (# of questions with N unique levels)
    #>
    #> price: Continuous variable
    #>   Questions by # unique levels:
    #>   1 (complete overlap):  16.7%  (100 / 600 questions)
    #>   2 (partial overlap):   83.3%  (500 / 600 questions)
    #>   3 (partial overlap):    0.0%  (0 / 600 questions)
    #>   4 (partial overlap):    0.0%  (0 / 600 questions)
    #>   5 (no overlap):         0.0%  (0 / 600 questions)
    #>   Average unique levels per question: 1.83
    #>
    #> typeGala: Continuous variable
    #>   Questions by # unique levels:
    #>   1 (complete overlap):  33.3%  (200 / 600 questions)
    #>   2 (no overlap):        66.7%  (400 / 600 questions)
    #>   Average unique levels per question: 1.67
    #>
    #> typeHoneycrisp: Continuous variable
    #>   Questions by # unique levels:
    #>   1 (complete overlap):  50.0%  (300 / 600 questions)
    #>   2 (no overlap):        50.0%  (300 / 600 questions)
    #>   Average unique levels per question: 1.50
    #>
    #> freshnessAverage: Continuous variable
    #>   Questions by # unique levels:
    #>   1 (complete overlap):  16.7%  (100 / 600 questions)
    #>   2 (no overlap):        83.3%  (500 / 600 questions)
    #>   Average unique levels per question: 1.83
    #>
    #> freshnessExcellent: Continuous variable
    #>   Questions by # unique levels:
    #>   1 (complete overlap):  33.3%  (200 / 600 questions)
    #>   2 (no overlap):        66.7%  (400 / 600 questions)
    #>   Average unique levels per question: 1.67

## Customizing Optimization

The `cbc_design()` function offers many customization options:

    # Advanced stochastic design with custom settings
    design_advanced <- cbc_design(
      profiles = profiles,
      n_alts = 2,
      n_q = 8,
      n_resp = 300,
      n_blocks = 2,
      priors = priors,
      method = "stochastic",
      n_start = 10, # More starting points for better optimization
      max_iter = 100, # More iterations per start
      n_cores = 4, # Parallel processing
      remove_dominant = TRUE,
      dominance_threshold = 0.9,
      randomize_questions = TRUE,
      randomize_alts = TRUE
    )

# Next Steps

After generating your design:

1. Inspect the design using `cbc_inspect()` to understand its properties
2. Simulate choices using `cbc_choices()` to test the design
3. Conduct power analysis using `cbc_power()` to determine sample size requirements
4. Compare alternatives using `cbc_compare()` to choose the best design

For more details on these next steps, see:

- The Simulating Choices vignette
- The Power Analysis vignette