  Longitudinal processes often pose nonlinear change patterns. Latent basis
growth models (LBGMs) provide a versatile solution without requiring specific
functional forms. Building on the LBGM specification for unequally-spaced waves
and individual occasions proposed by Liu and Perera (2023), we extend LBGMs to
multivariate longitudinal outcomes. This provides a unified approach to
nonlinear, interconnected trajectories. Simulation studies demonstrate that the
proposed model can provide unbiased and accurate estimates with target coverage
probabilities for the parameters of interest. Real-world analyses of reading
and mathematics scores demonstrates its effectiveness in analyzing joint
developmental processes that vary in temporal patterns. Computational code is
included.
