Heart rate variability (HRV) can contain useful information about a subject, but its derivation traditionally relies on conductive electrocardiography (ECG) with adhesive electrodes. While photoplethysmography (PPG) can be acquired in much less intrusive ways, its signal differs fundamentally from ECG. First, it represents mechanical cardiac activity instead of electrical. Second, fiducial points of its waveform are much smoother compared to the QRS complex of the ECG. Still, studies have shown that meaningful HRV parameters can be extracted using PPG which small differences compared to ECG. In this work, we evaluate an algorithm termed "continuous local interval estimator (CLIE)" that analyzes the signal\'s entire waveform instead of individual fiducial points with respect to its potential in deriving beat-to-beat intervals and the time-domain HRV parameters SDNN, RMSSD, and pNN50 from the PPG. For evaluation, a polysomnography dataset consisting of more than 900,000 recorded heart beats from 33 subjects was used. The performance of CLIE was compared to three peak-detection strategies (peak-to-peak, peak-to-peak of first derivative, troth-to-troth) often found in the literature. For interval estimation and the proposed HRV parameters, CLIE outperformed the reference methods in terms of accuracy. Moreover, when the signal was contaminated with simulated noise, the performance of CLIE was affected only minimally compared to the references. While an adaptive prior could increase the performance of CLIE for very noisy signals, its application was found to deteriorate results when no noise was added. Thus, CLIE was found to be an accurate and robust tool when deriving HRV parameters from PPG signals, which can be augmented by an adaptive prior for potentially noisy signals, such as PPG imaging or wearable PPG.