This package creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). MixSIAR is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. MixSIAR incorporates several years of advances since MixSIR and SIAR'.

Allows to analyze the reading data obtained with the MNREAD Acuity Chart, a continuous-text reading acuity chart for normal and low vision. Provides the necessary functions to plot the MNREAD curve and estimate automatically the four MNREAD parameters: Maximum Reading Speed, Critical Print Size, Reading Acuity and Reading Accessibility Index. Parameters can be estimated either with the standard method or with a nonlinear mixed-effects (NLME) modeling. See Calabrese et al. 2018 for more details <doi:10.1167/18.1.8>.

This package performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.

Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping spatstat.geom functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B). For simple integration along the axes, the cubature package is more appropriate.

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".

This package provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. This method build upon established R packages, including Hofert et al'. (2025) <doi:10.32614/CRAN.package.copula>, Scott (2024) <doi:10.32614/CRAN.package.bsts>, and Stephenson et al'. (2024) <doi:10.32614/CRAN.package.evd>.

This package provides tools for exploring effects of correlated features in predictive models. The predict_triplot() function delivers instance-level explanations that calculate the importance of the groups of explanatory variables. The model_triplot() function delivers data-level explanations. The generic plot function visualises in a concise way importance of hierarchical groups of predictors. All of the the tools are model agnostic, therefore works for any predictive machine learning models. Find more details in Biecek (2018) <arXiv:1806.08915>.

Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on canonical analysis. These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>.

This package contains implementation of DecontX (Yang et al. 2020), a decontamination algorithm for single-cell RNA-seq, and DecontPro (Yin et al. 2023), a decontamination algorithm for single cell protein expression data. DecontX is a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. DecontPro is a Bayesian method that estimates the level of contamination from ambient and background sources in CITE-seq ADT dataset and decontaminate the dataset.

This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences.

VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories.

Rakarrack is a richly featured multi-effects processor emulating a guitar effects pedalboard. Effects include compressor, expander, noise gate, equalizers, exciter, flangers, chorus, various delay and reverb effects, distortion modules and many more. Most of the effects engine is built from modules found in the excellent software synthesizer ZynAddSubFX. Presets and user interface are optimized for guitar, but Rakarrack processes signals in stereo while it does not apply internal band-limiting filtering, and thus is well suited to all musical instruments and vocals.

Subject recruitment for medical research is challenging. Slow patient accrual leads to delay in research. Accrual monitoring during the process of recruitment is critical. Researchers need reliable tools to manage the accrual rate. This package provides an implementation of a Bayesian method that integrates researcher's experience on previous trials and data from the current study, providing reliable prediction on accrual rate for clinical studies. It provides functions for Bayesian accrual prediction which can be easily used by statisticians and clinical researchers.

This package lets you generate planar and spherical triangle meshes, compute finite element calculations for 1- and 2-dimensional flat and curved manifolds with associated basis function spaces, methods for lines and polygons, and transparent handling of coordinate reference systems and coordinate transformation, including sf and sp geometries. The core fmesher library code was originally part of the INLA package, and implements parts of "Triangulations and Applications" by Hjelle and Daehlen (2006) <doi:10.1007/3-540-33261-8>.

We provide several avenues to predict and account for user-based mortality and tag loss during mark-recapture studies. When planning a study on a target species, the retentionmort_generation() function can be used to produce multiple synthetic mark-recapture datasets to anticipate the error associated with a planned field study to guide method development to reduce error. Similarly, if field data was already collected, the retentionmort() function can be used to predict the error from already generated data to adjust for user-based mortality and tag loss. The test_dataset_retentionmort() function will provide an example dataset of how data should be inputted into the function to run properly. Lastly, the retentionmort_figure() function can be used on any dataset generated from either model function to produce an rmarkdown printout of preliminary analysis associated with the model, including summary statistics and figures. Methods and results pertaining to the formation of this package can be found in McCutcheon et al. (in review, "Predicting tagging-related mortality and tag loss during mark-recapture studies").

This package implements the deflist class, a read-only list-like object that accesses its elements via a function. The deflist class can be used to model deferred access to data or computations by routing indexed list access to a function. This approach is particularly useful when sequential list-like access to data is required but holding all the data in memory at once is not feasible. The package also provides utilities for memoisation and caching to optimize access to frequently requested elements.

Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.

This package provides a handy collection of utility functions designed to aid in package development, plotting and scientific research. Package development functionalities includes among others tools such as cross-referencing package imports with the description file, analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub. Some of the other functionalities include automatic package installation and loading, plotting points without overlap, creating nice breaks for plots, overview tables and many more handy utility functions.

An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>. The package also implements the nonparametric ordinal regression model described in Saarela, Rohrbeck & Arjas <DOI:10.1214/22-BA1310>.

Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.

This package provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200).

Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results. Note: The package persval is independently developed based on the personal values theoretical framework, and is not directly endorsed by professor Schwartz.

Shadow Document Object Model is a web standard that offers component style and markup encapsulation. It is a critically important piece of the Web Components story as it ensures that a component will work in any environment even if other CSS or JavaScript is at play on the page. Custom HTML tags can't be directly identified with selenium tools, because Selenium doesn't provide any way to deal with shadow elements. Using this plugin you can handle any custom HTML tags.

COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.