Identify 17 Sustainable Development Goals and associated 169 targets in text.

Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025).

The Slow Digestibility Index (SDI) is a tool that helps users evaluate the slow-digestion properties of crops or food matrices by combining multiple factors into a single score. It considers parameters related to starch composition [total starch (TS), amylose/amylopectin ratio (Aratio), total amylose content (TAC), and total amylopectin content (TAPC)], starch digestibility [rapidly digestible starch (RDS), slowly digestible starch (SDS) and resistant starch (RS)], structural properties [relative crystallinity (RC)], non-starch components [total protein, total oil content (TOC), and total phenolic content (TPC)], and pasting behaviour [peak viscosity (PV), pasting temperature (PT), holding strength (HS), and final viscosity (FV)].The SDI is flexible and allows users to calculate the index using all parameters or only selected ones, depending on the data available. Users can also compute a starch-based SDI (using only starch-related parameters) or a principal component analysis (PCA)-based SDI, where weights are determined automatically from the data. Thus, the SDI provides a simple way to compare and rank crops or food samples based on their slow digestion potential. The package implements SDI(),starchSDI(), genSDI(), scoreSDI(), and pcaSDI() for estimating slow digestibility index using predefined weighted TOPSIS, starch-specific TOPSIS, user-defined weighted TOPSIS, score-based normalization, and PCA based approaches, respectively. The package has been developed using the algorithm of Pandey et al. (2026) <doi:10.1016/j.jff.2026.107208>.