Possible reasons why you get different results from the two packages include

• The default of glmer() is the Laplace approximation and not the adaptive Gaussian quadrature. You could try refitting with glmer() and increase the nAGQ argument.
• The optimization procedure in one of the two packages was not completely successful. You could try fitting the model with both functions by changing the defaults or providing better starting values.

In general generalized linear mixed models are more challenging models to fit, resulting in the observation you made. Therefore, it is advisable to study how each package work and suitably tweak the defaults.

Possible reasons why you get different results from the two packages include

• The default of glmer() is the Laplace approximation and not the adaptive Gaussian quadrature. You could try refitting with glmer() and increase the nAGQ argument.
• The optimization procedure in one of the two packages was not completely successful. You could try fitting the model with both functions by changing the defaults or providing better starting values.

In general generalized linear mixed models are more challenging models to fit, resulting in the observation you made. Therefore, it is advisable to study how each package works and suitably tweak the defaults.