Package: projoint 1.1.1

projoint: Conjoint Analysis with Reliability Correction and Visualization

Provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.

Authors:Yusaku Horiuchi [aut, cre], Aaron Kaufman [aut], Gary King [aut]

projoint_1.1.1.tar.gz
projoint_1.1.1.zip(r-4.7)projoint_1.1.1.zip(r-4.6)projoint_1.1.1.zip(r-4.5)
projoint_1.1.1.tgz(r-4.6-any)projoint_1.1.1.tgz(r-4.5-any)
projoint_1.1.1.tar.gz(r-4.7-any)projoint_1.1.1.tar.gz(r-4.6-any)
projoint_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
projoint/json (API)

# Install 'projoint' in R:
install.packages('projoint', repos = c('https://yhoriuchi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/yhoriuchi/projoint/issues

Pkgdown/docs site:https://yhoriuchi.github.io

Datasets:
  • exampleData1 - Projoint Example Data Set 1: Building Conjoint with a Repeated, Flipped Task
  • exampleData1_labelled_tibble - Projoint Example Data Set 1: "Labelled Tibble"
  • exampleData2 - Projoint Example Data Set 2: Building Conjoint with a Repeated, Non-Flipped Task
  • exampleData3 - Projoint Example Data Set 3: Building Conjoint with No Repeated Task
  • out1_arranged - Example Output: Manually Rearranged Labels

On CRAN:

Conda:

amceconjointconjoint-analysisconjoint-experimentsconjoint-survey-experimentsmarginal-meansmeasurement-error

8.31 score 25 stars 39 scripts 324 downloads 8 exports 46 dependencies

Last updated from:9f4ed714c6. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK235
source / vignettesOK244
linux-release-x86_64OK192
macos-release-arm64OK93
macos-oldrel-arm64OK105
windows-develOK113
windows-releaseOK136
windows-oldrelOK120
wasm-releaseOK180

Exports:make_projoint_datapredict_tauprojointread_labelsread_Qualtricsreshape_projointsave_labelsset_qoi

Dependencies:bitbit64clicliprcpp11crayondplyrestimatrfarverforcatsFormulagenericsggplot2ggthemesgluegtablehmsisobandlabelinglifecyclemagrittrMASSpillarpkgconfigprettyunitsprogresspurrrR6RColorBrewerRcppRcppEigenreadrrlangS7scalesstringistringrtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr

Explore and Compare Further
🌟 Explore and Compare Further | 📦 Setup | ⚖️ Explore: Compare Trade-offs Directly | 🧩 Explore: Compare Multiple Levels Simultaneously | 📊 Compare: Subgroup Differences
Rendered fromexplore.Rmdusingknitr::rmarkdown

Last update: 2026-02-22
Started: 2025-02-06

Read and Wrangle Your Data
📥 Read Your Data | 🚀 Read Workflow | 🛠️ Wrangle Your Data | 🚀 Wrangle Workflow | Example (Flipped Repeated Task)
Rendered fromread.Rmdusingknitr::rmarkdown

Last update: 2026-02-20
Started: 2025-04-20

Choice-Level Analysis
Why Choice-Level Analysis? | ⚠️ Problems with Profile-Level Analysis | ✅ Advantages of Choice-Level Analysis | Key Issues and Applications | Why Move to Choice-Level Analysis? | Summary | Key Takeaway | 📚 References
Rendered fromstructure.Rmdusingknitr::rmarkdown

Last update: 2025-10-26
Started: 2023-08-15

FAQ Page
What's the difference between "profile-level" and "choice-level" conjoint analysis?? | What if I have profile-level data? | 📈 Profile-Level MMs and AMCEs | Is there other conjoint software?? | 1. Generate a JavaScript or PHP randomizer | JavaScript | PHP | 2. Modify your JavaScript or PHP randomizer | 3. Add conjoint tables with embedded fields in Qualtrics | Adding a repeated task (recommended!) | 4. Sample Qualtrics survey template
Rendered fromfaq.Rmdusingknitr::rmarkdown

Last update: 2025-10-26
Started: 2025-06-05

Analyze and Visualize Important QOIs
🎯 Estimate Corrected MMs or AMCEs | 📦 Prepare Example Data | 🛠️ Why Use IDs (e.g., att1, level1)? | 📈 Estimate Marginal Means (MMs) | 📉 Estimate AMCEs | 🔎 Predict Intra-Respondent Reliability (IRR) | 🎨 Visualize MMs or AMCEs | ⚖️ Choice-Level Analysis
Rendered fromanalyze.Rmdusingknitr::rmarkdown

Last update: 2025-09-10
Started: 2025-04-20

Design Your Survey
Set Up Your Survey Using the Projoint Survey Design Tool | How to Use the Projoint Survey Design Tool | Other Options
Rendered fromdesign.Rmdusingknitr::rmarkdown

Last update: 2025-08-20
Started: 2025-04-20

Correct Measurement-Error Bias
Why Correct for Measurement Error? | The Consequences of Ignoring Measurement Error | How projoint Corrects for Measurement Error | Key Takeaway | 📚 Key Reference
Rendered fromcorrect.Rmdusingknitr::rmarkdown

Last update: 2025-04-21
Started: 2025-04-20