Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>, and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.

This package provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the nimble modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

This package performs nonparametric tests for equality of location against ordered alternatives.

Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) <https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.

Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more. NCutYX allows clustering of variables, of samples, and both variables and samples (biclustering), while incorporating the dependencies across multiple types of Omics data. (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).

Species Identification using DNA Barcodes Integrated with Environmental Niche Models.

This package provides methods to estimate finite-population parameters under nonresponse that is not missing at random (NMAR, nonignorable). Incorporates auxiliary information and user-specified response models, and supports independent samples and complex survey designs via objects from the survey package. Provides diagnostics and optional variance estimates. For methodological background see Qin, Leung and Shao (2002) <doi:10.1198/016214502753479338> and Riddles, Kim and Im (2016) <doi:10.1093/jssam/smv047>.

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.

Exact automatic differentiation for R functions. Provides a composable derivative operator D that computes gradients, Hessians, Jacobians, and arbitrary-order derivative tensors at machine precision. D(D(f)) gives Hessians, D(D(D(f))) gives third-order tensors for skewness of maximum likelihood estimators, and so on to any order. Works through any R code including loops, branches, and control flow.

This package provides a fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

Extends package nat (NeuroAnatomy Toolbox) by providing objects and functions for handling template brains.

Due to Rstudio's status as open source software, we believe it will be utilized frequently for future data analysis by users whom lack formal training or experience with R'. The NMVANOVA (Novice Model Variation ANOVA) a streamlined variation of experimental design functions that allows novice Rstudio users to perform different model variations one-way analysis of variance without downloading multiple libraries or packages. Users can easily manipulate the data block, and needed inputs so that users only have to plugin the four designed variables/values.

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers. See Konietschke et al. (2015) <doi:10.18637/jss.v064.i09> for details.

Sends queries to a specified Neo4J graph database, capturing results in a dataframe where appropriate. Other useful functions for the importing and management of data on the Neo4J server and basic local server admin.

Social network analysis has become an essential tool in the study of complex systems. NetExplorer allows to visualize and explore complex systems. It is based on d3js library that brings 1) Graphical user interface; 2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.

This is a pure dummy interfaces package which mirrors MsSparkUtils APIs <https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/microsoft-spark-utilities?pivots=programming-language-r> of Azure Synapse Analytics <https://learn.microsoft.com/en-us/azure/synapse-analytics/> for R users, customer of Azure Synapse can download this package from CRAN for local development.

Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.

Stacking arrays according to dimension names, subset-aware splitting and mapping of functions, intersecting along arbitrary dimensions, converting to and from data.frames, and many other helper functions.

Calculation of molecular number and brightness from fluorescence microscopy image series. The software was published in a 2016 paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for the technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>. A review of the technique was published in 2017 <doi:10.1016/j.ymeth.2017.12.001>.

This package provides tools for the structured processing of PET neuroimaging data in preparation for the estimation of Simultaneous Confidence Corridors (SCCs) for one-group, two-group, or single-patient vs group comparisons. The package facilitates PET image loading, data restructuring, integration into a Functional Data Analysis framework, contour extraction, identification of significant results, and performance evaluation. It bridges established packages (e.g., oro.nifti') with novel statistical methodologies (e.g., ImageSCC') and enables reproducible analysis pipelines, including comparison with Statistical Parametric Mapping ('SPM').

This package provides a set of convenience functions as well as geographical/political data about Nigeria, aimed at simplifying work with data and information that are specific to the country.

This package performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

Base package for Neuroconductor', which includes many helper functions that interact with objects of class nifti', implemented by package oro.nifti', for reading/writing and also other manipulation functions.