一週瀏覽器分頁

諸君：

## Ideas

• Gamma Regression
• [Pólya-Gamma Augmentation](https://gregorygundersen.com/blog/2019/09/20/polya-gamma/)
• [Bayesian nonparametrics in NIMBLE: Density estimation](https://r-nimble.org/nimbleExamples/bnp_density.html)
• [Minjeong Jeon](https://orcid.org/0000-0002-5880-4146)
• [Programming an IRT, 2Pl Model from Scratch](https://www.r-bloggers.com/2019/06/irt-from-scratch/)
• https://openmx.ssri.psu.edu/docs/OpenMx/latest/FactorAnalysis_Matrix.html#
• A function from runjags [template.jags](https://rdrr.io/cran/runjags/man/template.jags.html)

## Papers

(irt, lsirm)

• 通過 Pólya-Gamma 分布的邏輯試題反應模型的 Gibbs 采樣器： 一種計算高效的數據增量策略。
  Jiang, Z., & Templin, J. (2019). Gibbs samplers for logistic item response models via the Pólya–Gamma distribution: A computationally efficient data-augmentation strategy. Psychometrika, 84(2), 358-374.
• lsirm12pl: 一個用來做潛在空間試題反應模型的 R 包。
  Go, D., Park, J., Park, J., Jeon, M., & Jin, I. H. (2022). lsirm12pl: An R package for latent space item response modeling. arXiv preprint arXiv:2205.06989
• 描繪無法觀察到的題目與作答者間交互作用關係：帶有交互作用圖的潛在空間試題反應模型。
  Jeon, M., Jin, I. H., Schweinberger, M., & Baugh, S. (2021). Mapping unobserved item–respondent interactions: a latent space item response model with interaction map. Psychometrika, 86(2), 378-403.
• 探索反應與反應時間之間條件依賴關系的潛空間擴散項目反應理論模型。
  Kang, I., Jeon, M., & Partchev, I. (2023). A Latent Space Diffusion Item Response Theory Model to Explore Conditional Dependence between Responses and Response Times. Psychometrika, 1-35.
• 從數據中學習屬性層次： 兩種探索性方法。
  Wang, C., & Lu, J. (2021). Learning attribute hierarchies from data: Two exploratory approaches. Journal of Educational and Behavioral Statistics, 46(1), 58-84.
• 用項目反應樹模型評估項目特徵效應。
  Böckenholt, U. (2019). Assessing item‐feature effects with item response tree models. British Journal of Mathematical and Statistical Psychology, 72(3), 486-500.
• 校正極端反應風格：模型選擇的重要性。
  Schoenmakers, M., Tijmstra, J., Vermunt, J., & Bolsinova, M. (2023). Correcting for Extreme Response Style: Model Choice Matters. Educational and Psychological Measurement, 00131644231155838.
• 評論：探索項目反應樹數據中的條件依賴關係。
  Jeon, M. (2023). Commentary: Explore Conditional Dependencies in Item Response Tree Data. Psychometrika, 1-6.
• 反應時間與準確性之間的條件依賴性建模。
  Bolsinova, M., De Boeck, P., & Tijmstra, J. (2017). Modelling conditional dependence between response time and accuracy. Psychometrika, 82, 1126-1148.
• 研究心理測量數據的順序反應和反應時間的心理過程的建模框架。
  Kang, I., Molenaar, D., & Ratcliff, R. (2023). A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data. Psychometrika, 1-35.
• 認知診斷模型中的潛結構和層次結構學習。
  Ma, C., Ouyang, J., & Xu, G. (2023). Learning latent and hierarchical structures in cognitive diagnosis models. psychometrika, 88(1), 175-207.
• 測量學習環境中差異成長的廣義縱向混合 IRT 模型。
  Kadengye, D. T., Ceulemans, E., & Van den Noortgate, W. (2014). A generalized longitudinal mixture IRT model for measuring differential growth in learning environments. Behavior research methods, 46, 823-840.
• 創建二維潛在空間的測驗訊息剖面圖。
  Ackerman, T. A. (1994). Creating a test information profile for a two-dimensional latent space. Applied psychological measurement, 18(3), 257-275.
• 介紹用 pyBKT 進行貝氏知識追蹤。
  Bulut, O., Shin, J., Yildirim-Erbasli, S. N., Gorgun, G., & Pardos, Z. A. (2023). An Introduction to Bayesian Knowledge Tracing with pyBKT. Psych, 5(3), 770–786. https://doi.org/10.3390/psych5030050
• 貝氏正則化 SEM：當前能力與限制。
  van Erp, S. (2023). Bayesian Regularized SEM: Current Capabilities and Constraints. Psych, 5(3), 814–835. https://doi.org/10.3390/psych5030054
• 用於聯合分析的具有 DP 先驗的貝氏半參數潛在變項模型：用 nimble 實現。
  Ma, Z., & Chen, G. (2020). Bayesian semiparametric latent variable model with DP prior for joint analysis: Implementation with nimble. Statistical Modelling, 20(1), 71-95.

(modeling)

• 在使用 BUGS/JAGS 的混合效應位置尺度模型中模擬人內消極和積極情感變化的個體差異。
  Rast, P., Hofer, S. M., & Sparks, C. (2012). Modeling individual differences in within-person variation of negative and positive affect in a mixed effects location scale model using BUGS/JAGS. Multivariate Behavioral Research, 47(2), 177-200.
• 具有異質性和偏度的縱向數據的非線性混合效應混合模型的貝氏分析。
  Lu, X., & Huang, Y. (2014). Bayesian analysis of nonlinear mixed‐effects mixture models for longitudinal data with heterogeneity and skewness. Statistics in medicine, 33(16), 2830-2849.
• 分歧與維度：用 Beta 過程方法估計美國國會的理想點。
  McAlister, K. Disagreement and Dimensionality: A Beta Process Approach to Estimating Dimensionality of Ideal Points in the US Congress.
• OpenMx 2.0：擴充版結構方程式和統計建模。
  Neale, M. C., Hunter, M. D., Pritikin, J. N., Zahery, M., Brick, T. R., Kirkpatrick, R. M., ... & Boker, S. M. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81, 535-549.
• 潛在曲線模式和潛在改變分數模式估計用 R。
  Ghisletta, P., & McArdle, J. J. (2012). Latent curve models and latent change score models estimated in R. Structural equation modeling: a multidisciplinary journal, 19(4), 651-682.
• 貝氏架構下的成長曲線模式適配。
  Oravecz, Z., & Muth, C. (2018). Fitting growth curve models in the Bayesian framework. Psychonomic Bulletin & Review, 25, 235-255.
• 用於高維資料分析的 Spike‐and‐slab 最小絕對收縮和選擇算子廣義加法模型及可擴展算法。
  Guo, B., Jaeger, B. C., Rahman, A. F., Long, D. L., & Yi, N. (2022). Spike‐and‐slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high‐dimensional data analysis. Statistics in Medicine, 41(20), 3899-3914.

(SEL)

• 社會認知如何影響社會決策。
  Lee, V. K., & Harris, L. T. (2013). How social cognition can inform social decision making. Frontiers in neuroscience, 7, 259.
• 量表開發研究： 社會情感學習量表--青壯年版本（SELS-YF）。
  ÖZDEMİR, N. K., & Büyükçolpan, H. (2021). A scale development study: Social emotional learning scale-young adult form (SELS-YF). Kastamonu Eğitim Dergisi, 29(4), 205-218.

Remark

See Also

Best,

JW