Software developed by the Latent Class Methods research team headed by John Hanfelt & Limin Peng, Dept. of Biostatistics & Bioinformatics at Emory University
SLTCA
The first latent trajectory class analysis technique based on artificial likelihood concepts that avoids undue modeling assumptions and is computationally tractable. For more details, see Hart, Fei & Hanfelt (2021): Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics 77: 1118-1128.
- SAS macro written by Kari Hart and John Hanfelt. Please refer to the comments at the beginning of the sltca.sas file for instructions on running the macro.