I'm about to attempt to fit a non-linear mixed effects model $(A + B*e^t)$ in lme4. I've already tried fitting this model in nlme with some difficulty due to noise within the data. However, I understand that lme4 has more robust algorithms that's able to better fit non-linear models such as the one written above.

I've ready a few posts such as How to choose nlme or lme4 R library for mixed effects models?

My question: Is lme4 better than nlme with non-linear models? (nlme vs nlmer)

I'm about to attempt to fit a non-linear mixed effects model $(A + B*e^t)$ in lme4. I've already tried fitting this model in nlme with some difficulty due to noise within the data. However, I understand that lme4 has more robust algorithms that's able to better fit non-linear models such as the one written above.

I've ready a few posts such as How to choose nlme or lme4 R library for mixed effects models?

My question: Is lme4 better than nlme with non-linear models? (nlme vs nlmer)

I'm about to attempt to fit a non-linear mixed effects model $(A + B*e^t)$ in lme4. I've already tried fitting this model in nlme with some difficulty due to noise within the data. However, I understand that lme4 has more robust algorithms that's able to better fit non-linear models such as the one written above.

I've ready a few posts such as How to choose nlme or lme4 R library for mixed effects models?

My question: Is it possible to fit non-linear models to longitudinal data in lme4?

I'm about to attempt to fit a non-linear mixed effects model $(A + B*e^t)$ in lme4. I've already tried fitting this model in nlme with some difficulty due to noise within the data. However, I understand that lme4 has more robust algorithms that's able to better fit non-linear models such as the one written above.

I've ready a few posts such as How to choose nlme or lme4 R library for mixed effects models?

My question: Is lme4 better than nlme with non-linear models? (nlme vs nlmer)