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>.

An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via TMB (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

This package provides functions to parse and analyze logs generated by ShinyProxy containers. It extracts metadata from log file names, reads log contents, and computes summary statistics (such as the total number of lines and lines containing error messages), facilitating efficient monitoring and debugging of ShinyProxy deployments.

This package provides methods for regression with high-dimensional predictors and univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.

Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.

Use piping, verbs like group_by and summarize', and other dplyr inspired syntactic style when calculating summary statistics on survey data using functions from the survey package.

This package provides Stable Diffusion image generation using the ggmlR library, with no Python or external API dependencies. Supports text-to-image and image-to-image generation for SD 1.x, SD 2.x, SDXL', Flux, and FLUX.2'. A single sd_generate() function handles the entire pipeline, including sampling and high-resolution output. Features multi-GPU support, a Shiny GUI, and runs on CPU or Vulkan GPU across Linux, macOS, and Windows.

SPINA (Structure Parameter Inference Approach) is a methodology to calculate constant structure parameters of endocrine homeostatic systems from steady-state hormone and metabolite concentrations. Methods and equations for thyroid homeostasis (SPINA Thyr) have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>, and for glucose homeostasis (SPINA Carb) in Dietrich et al. (2022) <doi:10.1038/s41598-022-22531-3> and Dietrich et al. (2024) <doi:10.1111/1753-0407.13525>.

Edit SVG files created in Inkscape by replacing placeholders (e.g. a rectangle element or in a text box) by ggplot2 objects, images or text. This helps automate the creation of figures with complex layouts.

This package provides scaffolding tools to help researchers prepare and submit computational jobs to high-throughput computing (HTC) schedulers. Generates the files required to run containerized R analyses on HTCondor', including submit files and executable scripts, and wraps the system commands needed to stage files, submit jobs, monitor status, and retrieve results from a CHTC submit node. Provides htc_config() for managing connection details and SSH connection reuse guidance. Works naturally alongside containr for container image management and toolero for dataset splitting and project scaffolding.

Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

Finds causal connections in precision data, finds lags and embeddings in time series, guides training of neural networks and other smooth models, evaluates their performance, gives a mathematically grounded answer to the over-training problem. Smooth regression is based on the Gamma test, which measures smoothness in a multivariate relationship. Causal relations are smooth, noise is not. sr includes the Gamma test and search techniques that use it. References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.

Procedure to optimally split a dataset for training and testing. SPlit is based on the method of support points, which is independent of modeling methods. Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for details. This work is supported by U.S. National Science Foundation grant DMREF-1921873.

Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.

Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). The package also includes data for the first edition, Stat2: Building Models for a World of Data and a few functions for plotting diagnostics.

This package provides routines for scoring behavioral questionnaires. Includes scoring procedures for the International Physical Activity Questionnaire (IPAQ) <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.

Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://www.ijsr.net/archive/v4i4/SUB152869.pdf>.

Identifying cell types based on expression profiles is a pillar of single cell analysis. scROSHI identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Michael Prummer, Anne Bertolini, Lars Bosshard, Florian Barkmann, Josephine Yates, Valentina Boeva, The Tumor Profiler Consortium , Daniel Stekhoven, Franziska Singer, scROSHI: robust supervised hierarchical identification of single cells, NAR Genomics and Bioinformatics, Volume 5, Issue 2, June 2023, lqad058, <doi:10.1093/nargab/lqad058>.

This package provides functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.

This package provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping. More details are available in: Parast, L. and Bartroff, J (2024) <doi:10.1093/biomtc/ujae108>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateseq>. A Shiny App implementing the methods can be found at <https://parastlab.shinyapps.io/SurrogateSeqApp/>.

This package implements the shared keyboard design (SKBD) for model-assisted phase I dose-finding, including decision-boundary construction, operating-characteristic simulation, and extensions for dose insertion and time-to-event settings. The package also provides an interactive Shiny interface for trial-planning workflows. For more details, see Zhao, Shi, and Xu (2026) <doi:10.48550/arXiv.2605.25043>.

The sparse vector field consensus (SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.