Dendrochronology plays an important role in estimating past climatic conditions and predicting future climate change. Detrending and chronology development are the fundamental steps of the study of dendrochronology. ARSTAN is the most popular program used to accomplish this step, and it has played an important role in the development of dendrochronology. However, ARSTAN uses the Fortran programming language, so users find it difficult to understand and revise the algorithm of the source program to meet their needs. An emerging package of the R language named dplR provides similar functions to ARSTAN. R and dplR's source code is fully open to the public; thus, it has numerous users. When scholars from different domains communicate and share the methods and results of dendrochronology, it can help them improve those chronologies. In addition, R and dplR have become a good supplement to traditional analysis software. This paper compares the different dendrochronological analysis algorithms and results provided by ARSTAN and dplR with tree ring width data from Picea crassifolia on Helan Mountain, Ningxia Hui Autonomous Region, China. The results show that the two programs calculated exactly the same means and standard deviations. The mean error of the mean sensitivities (MS) and first-order autocorrelations (AC) were 0.005 and 0.008, respectively, but they had a clear conversion relationship. When using the same method for detrending with both types of software, the parameters of fitting curves were generally equal, and the corresponding standard chronologies developed by the two programs had a mean error of only 0.002. However, the residual chronologies were very different. In the time domain, a significant difference was observed in the residual chronologies in the first 20-30 years. In the frequency domain, the residual chronologies created using ARSTAN showed more low frequency information than that created using dplR. For example, the former showed periods of 32 years with higher power than those of dplR. In the common interval analysis, ARSTAN gave a higher expressed population signal (EPS) and signal-to-noise ratio (SNR) of chronologies than dplR. EPS error was 0.4% and SNR error was 30%-40%. By comparing the algorithms of the two programs, we found that ARSTAN and dplR have different initial value setting rules and nonlinear fitting methods to choose the best fitting model during detrending. When fitting an autoregression model, ARSTAN used a pooled algorithm to find the integral growing pattern and used the same fitting order for different sequences. However, dplR directly used different optimal fitting models for different sequences. In addition, the two programs used different, but similar, formulas for calculating MS, AC, EPS, and SNR. Although the absolute value of the results was different, calculation results of the same program using different data were comparable. In conclusion, this paper offers two suggestions for the meta-analysis of tree ring data from different sources. First, if the source data are available, researchers should choose a single program for statistical calculation, detrending, and common interval analysis based on their needs. Second, if the source data are not available, information related to the chronologies is sufficient; researchers should use only a single program to calculate EPS and SNR chronological statistics to ensure that the results will be comparable.