Psychometric network models from time-series and panel data. Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs) -- an undirected network model of partial correlations -- between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the extit{ts-lvgvar} when estimated from time-series data and the extit{panel-lvgvar} when estimated from panel data. These methods have been implemented in the software package extit{psychonetrics}, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.