Nii-C implements a framework of automatic parallel tempering Markov Chain Monte Carlo. Parameters ensure an efficient parallel tempering process that is set by a control system during the initial stages of a sampling process. The autotuned parameters consist of two parts: the temperature ladders of all parallel tempering Markov Chains, and the proposal distributions for all model parameters across all parallel tempering chains. Written in C, Nii-C supersedes the Python code Nii (ascl:2111.010). Nii-C is parallelized using the message-passing interface protocol to optimize the efficiency of parallel sampling, which facilitates rapid convergence in the sampling of high-dimensional and multimodal distributions, as well as the expeditious code execution time. The code can be used to trace complex distributions due to its high sampling efficiency and quick execution speed.

Nii implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) framework for sampling multidimensional posterior distributions and provides an observation simulation platform for the differential astrometric measurement of exoplanets. Although this code specifically focuses on the orbital parameter retrieval problem of differential astrometry, Nii can be applied to other scientific problems with different posterior distributions and offers many control parameters in the APT part to facilitate the adjustment of the MCMC sampling strategy; these include the number of parallel chains, the β values of different chains, the dynamic range of the sampling step sizes, and frequency of adjusting the step sizes.

Nii has been superseded by the C code Nii-C (ascl:2507.007).