This package provides GIS and map utilities, plus additional modeling tools for developing cellular automata, dynamic raster models, and agent based models in SpaDES'. Included are various methods for spatial spreading, spatial agents, GIS operations, random map generation, and others. See ?SpaDES.tools for an categorized overview of these additional tools. The suggested package NLMR can be installed from the following repository: (<https://PredictiveEcology.r-universe.dev>).

This package performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.

This package provides functions for the evaluation of surrogate endpoints when both the surrogate and the true endpoint are failure time variables. The approaches implemented are: (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at the first step and either a linear regression of log-hazard ratios at the second step (either adjusted or not for measurement error); (2) mixed proportional hazard models estimated via mixed Poisson GLM (Rotolo et al, 2017 <DOI:10.1177/0962280217718582>).

Calculates a modified Simplified Surface Energy Balance Index (SSEBI) and the Evaporative Fraction (EF) using geospatial raster data such as albedo and surface-air temperature difference (TSâ TA). The SSEBI is computed from albedo and TSâ TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) <doi:10.1016/S1464-1909(99)00128-8>.

Survival analysis models are commonly used in medicine and other areas. Many of them are too complex to be interpreted by human. Exploration and explanation is needed, but standard methods do not give a broad enough picture. survex provides easy-to-apply methods for explaining survival models, both complex black-boxes and simpler statistical models. They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023) <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.

This package provides confidence intervals in least-squares regressions when the variable of interest has a shift-share structure, and in instrumental variables regressions when the instrument has a shift-share structure. The confidence intervals implement the AKM and AKM0 methods developed in Adão, Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.

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.

Enables the ability to change or flash the title of the browser window during a shiny session.

This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2026) <doi:10.1016/j.jmva.2025.105536>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.

It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.

This package provides a topological version of k-NN: An abstract model is build as 2-dimensional self-organising map. Samples of unknown class are predicted by mapping them on the SOM and analysing class membership of neurons in the neighbourhood.

Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied to obtain parsimonious solutions. The method is described on Karimova, van Erp, Leenders, and Mulder (2024) <DOI:10.31234/osf.io/2g8qm>. Gibbs samplers are used for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace (lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and horseshoe priors (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>). These priors include an option for grouped regularization of different subsets of parameters (Meier et al., 2008; <DOI:10.1111/j.1467-9868.2007.00627.x>). F priors are used for the penalty parameters lambda^2 (Mulder and Pericchi, 2018 <DOI:10.1214/17-BA1092>). This correspond to half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).

This package provides tools for setting up ("design"), conducting, and evaluating large-scale simulation studies with graphics and tables, including parallel computations.

Allows the creation and manipulation of C++ std::vector's in R.

Identifies what optimal subset of a desired number of items should be retained in a short version of a psychometric instrument to assess the â broadestâ proportion of the construct-level content of the set of items included in the original version of the said psychometric instrument. Expects a symmetric adjacency matrix as input (undirected weighted network model). Supports brute force and simulated annealing combinatorial search algorithms.

Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.

This package contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.

Parse Standard Schedules Information file (types 2 and 3) into a Data Frame. Can also expand schedules into flights.

Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. <doi:10.1101/2021.04.27.21256104>.

Stock-and-flow models are a computational method from the field of system dynamics. They represent how systems change over time and are mathematically equivalent to ordinary differential equations. sdbuildR (system dynamics builder) provides an intuitive interface for constructing stock-and-flow models without requiring extensive domain knowledge. Models can quickly be simulated and revised, supporting iterative development. sdbuildR simulates models in R and Julia', where Julia offers unit support and large-scale ensemble simulations. Additionally, sdbuildR can import models created in Insight Maker (<https://insightmaker.com/>).

Automatically replaces "misspelled" words in a character vector based on their string distance from a list of words sorted by their frequency in a corpus. The default word list provided in the package comes from the Corpus of Contemporary American English. Uses the Jaro-Winkler distance metric for string similarity as implemented in van der Loo (2014) <doi:10.32614/RJ-2014-011>. The word frequency data is derived from Davies (2008-) "The Corpus of Contemporary American English (COCA)" <https://www.english-corpora.org/coca/>.

This is a graph database in SQLite'. It is inspired by Denis Papathanasiou's Python simple-graph project on GitHub'.

This is an interface for the Python package StepMix'. It is a Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods based on pseudolikelihood theory. Additional features include support for covariates and distal outcomes, various simulation utilities, and non-parametric bootstrapping, which allows inference in semi-supervised and unsupervised settings. Software paper available at <doi:10.18637/jss.v113.i08>.

This package provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.