To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as iCAMP (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for QPEN and added function icamp.cate() to summarize iCAMP results for different categories of taxa (e.g. core versus rare taxa).

Select set of parametric and non-parametric statistical tests. inferr builds upon the solid set of statistical tests provided in stats package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.

This package provides a fresh take on iterators in R. Designed to be cross-compatible with the iterators package, but using the nextOr method will offer better performance as well as more compact code. With batteries included: includes a collection of iterator constructors and combinators ported and refined from the iterators', itertools', and itertools2 packages.

Calculate false ring proportions from data frames of intra annual density fluctuations.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion. Example usage contains parallel runs, fitting power-laws, finite size scaling, computing autocorrelation, uncertainty analysis and plotting utilities.

Some functions for performing ICA, MICA, Group ICA, and Multilinear ICA are implemented. ICA, MICA/Group ICA, and Multilinear ICA extract statistically independent components from single matrix, multiple matrices, and single tensor, respectively. For the details of these methods, see the reference section of GitHub README.md <https://github.com/rikenbit/iTensor>.

ISO 3166-1 country codes and ISO 4217 currency codes provided by the International Organization for Standardization.

We consider the non-parametric maximum likelihood estimation of the underlying distribution function, assuming log-concavity, based on mixed-case interval-censored data. The algorithm implemented is base on Chi Wing Chu, Hok Kan Ling and Chaoyu Yuan (2024, <doi:10.48550/arXiv.2411.19878>).

This package provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi10.1037/1082-989X.1.1.30 and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi10.1214/aoms/1177732186.

The ISA is a biclustering algorithm that finds modules in an input matrix. A module or bicluster is a block of the reordered input matrix.

Computes bilateral and multilateral index numbers. It has support for many standard bilateral indexes as well as multilateral index number methods such as GEKS, GEKS-Tornqvist (or CCDI), Geary-Khamis and the weighted time product dummy (for details on these methods see Diewert and Fox (2020) <doi:10.1080/07350015.2020.1816176>). It also supports updating of multilateral indexes using several splicing methods.

Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting the algorithms. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the centers of clusters, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package inaparc contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package inaparc'.

Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets <arXiv:1304.1122>, evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.

This package provides a set of functions to analyse and compare texts, using classical text mining functions, as well as those from theoretical ecology.

This package implements multiple variants of the Information Bottleneck ('IB') method for clustering datasets containing continuous, categorical (nominal/ordinal) and mixed-type variables. The package provides deterministic, agglomerative, generalized, and standard IB clustering algorithms that preserve relevant information while forming interpretable clusters. The Deterministic Information Bottleneck is described in Costa et al. (2024) <doi:10.48550/arXiv.2407.03389>. The standard IB method originates from Tishby et al. (2000) <doi:10.48550/arXiv.physics/0004057>, the agglomerative variant from Slonim and Tishby (1999) <https://papers.nips.cc/paper/1651-agglomerative-information-bottleneck>, and the generalized IB from Strouse and Schwab (2017) <doi:10.1162/NECO_a_00961>.

Generates Personality Insights sunburst diagrams based on IBM Watson Personality Insights service output.

Helps with the thoughtful saving, reading, and management of result files (using rds files). The core functions take a list of parameters that are used to generate a unique hash to save results under. Then, the same parameter list can be used to read those results back in. This is helpful to avoid clunky file naming when running a large number of simulations. Additionally, helper functions are available for compiling a flat file of parameters of saved results, monitoring result usage, and cleaning up unwanted or unused results. For more information, visit the indexr homepage <https://lharris421.github.io/indexr/>.

Convenient functions to create ggplot2 graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).

This package provides tools for easily and flexibly creating ggplot2 maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code.

This package contains bibliographic information for the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, data generation, item and model fit analyse, local independence assumption, dimensionality assumption, wright map, characteristic and information curves under various models with a user-friendly Graphic User Interface.

Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.

Check if an externalptr is a null pointer. R does currently not have a native function for that purpose. This package contains a C function that returns TRUE in case of a null pointer.