Introduction | Log-multiplicative Assosciation Models | Relationship with Item Response Theory | The Pseudo-likelihood Algorithm | Alogrithm I: Estimation of $\lambda_{ij}$ and $\nu_{ijm}$ (or $a_{im}$) | Alogrithm II: Estimation of $\lambda_{ij}$s and $\sigma_{mm'}$ | Algorithm III: Estimation of $\lambda_{ij}$, $\nu_{ijm}$ (or $a_{im}$), and $\sigma_{mm'}$: | Dimensions | Model | Algorithm------------|------------------| --------------0 | Independence | II1 | Rasch family | II1 | GPCM | I1 | Nominal | I> 1 | Rasch family | II> 1 | GPCM | III> 1 | Nominal | III | The Package | Set Up | Install and Load Package | The Data | Basic syntax of 'ple.lma' | Example: $I=9$ items, $J=4$ categories, $N=250$ cases | Uni-dimensional Models: $M=1$ | Input | Output | Auxilary Functions | Multi-dimensional models, $M>1$ | Example: 42 items, N=1000, M=3 | Computational Time | Cases (N) | Items (I) | Dimensions (M) | Independence | Rasch | GPCM | Nominal----------|-----------|----------------|--------------|-------|--------|---------250 | 9 | 1 | 0.20 | 0.22 | 4.20 | 4.31250 | 9 | 3 | 0.20 | 0.27 | 13.04 | 13.33250 | 42 | 1 | 1.01 | 1.15 | 16.75 | 17.44250 | 42 | 3 | 2.24 | 2.51 | 59.11 | 59.101000 | 9 | 1 | 1.08 | 1.23 | 17.34 | 17.941000 | 9 | 3 | 1.04 | 1.31 | 47.34 | 47.681000 | 42 | 1 | 15.96 | 16.68 | 242.21 | 243.551000 | 42 | 3 | 12.75 | 16.83 | 435.51 | 465.47 | | Example: Dichotomous Items | Other Uses and Future Releases | Appendix | References