Map image classification efficacy (MICE) adjusts the accuracy rate relative to a random classification baseline (Shao et al. (2021)<doi:10.1109/ACCESS.2021.3116526> and Tang et al. (2024)<doi:10.1109/TGRS.2024.3446950>). Only the proportions from the reference labels are considered, as opposed to the proportions from the reference and predictions, as is the case for the Kappa statistic. This package offers means to calculate MICE and adjusted versions of class-level user's accuracy (i.e., precision) and producer's accuracy (i.e., recall) and F1-scores. Class-level metrics are aggregated using macro-averaging. Functions are also made available to estimate confidence intervals using bootstrapping and statistically compare two classification results.

This package provides functions to use the NamePrism API <https://www.name-prism.com/api> or NamSor API v2 <https://namsor.app/> for classifying names based on gender, 6 U.S. ethnicities, or 39 leaf nationalities. Updated to work with current API endpoints.

Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.

This package provides a flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the flipr package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" <doi:10.1111/rssc.12463>.

This package provides a collection of datasets related to neutrosophic sets for statistical modeling and analysis.

This package provides functions to construct a normative latent cognitive structure (N-LCS) from cognitive test data standardized to healthy controls, and to compute cognitive deviation magnitude (CDM) and cognitive deviation angle (CDA). Methods are described in Chen (2026) <doi:10.1080/23279095.2026.2691088>.

To add the table of numbers at risk below the Kaplan-Meier plot.

An interactive presentation on the topic of normal distribution using rmarkdown and shiny packages. It is helpful to those who want to learn normal distribution quickly and get a hands on experience. The presentation has a template for solving problems on normal distribution. Runtime examples are provided in the package function as well as at <https://kartikeyastat.shinyapps.io/NormalDistribution/>.

Non-linear shrinkage estimation of population eigenvalues and covariance matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).

Scrapes and cleans data from the NHL and ESPN APIs into data.frames and lists. Wraps 125+ endpoints documented in <https://github.com/RentoSaijo/nhlscraper/wiki> from high-level multi-season summaries and award winners to low-level decisecond replays and bookmakers odds, making them more accessible. Features cleaning and visualization tools, primarily for play-by-plays.

In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of ivregress in Stata with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.

Some functions for performing non-negative matrix factorization, non-negative CANDECOMP/PARAFAC (CP) decomposition, non-negative Tucker decomposition, and generating toy model data. See Andrzej Cichock et al (2009) and the reference section of GitHub README.md <https://github.com/rikenbit/nnTensor>, for details of the methods.

This package provides pseudo-weighted estimates of means and prevalences for finite population inference from nonprobability samples using auxiliary information from one or multiple probability reference surveys. The package supports estimation with multiple reference surveys, allowing auxiliary information to be combined when no single survey contains all variables relevant to participation. Optional cumulative precalibration can be applied to align weighted totals of shared variables across surveys. Methods are based on the generalized estimating equations framework of Landsman et al. (2026) <doi:10.1002/sim.70403> for correcting participation bias. For a single reference survey, the package implements the raking ratio calibration method and includes the adjusted logistic propensity (ALP) method of Wang, Valliant, and Li (2021) <doi:10.1002/sim.9122>, as well as the Chen-Li-Wu (CLW) method of Chen, Li, and Wu (2020) <doi:10.1080/01621459.2019.1677241>. Analytic variance estimation uses Taylor linearization and accounts for complex sampling designs in the reference surveys via integration with the survey package.

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>.

This package provides a comprehensive toolkit for statistical and machine learning-based analysis of long-term Natural Resource Management (NRM) datasets. Integrates formula-driven approaches, statistical inference, and machine learning (ML) models for advanced analytics. Modules cover trend and structural analysis (Mann-Kendall test, slope estimation, Chow test, structural break detection), multivariate system modelling (Partial Least Squares (PLS), Structural Equation Modelling (SEM)), response curve optimisation, time-series forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid models), panel data and treatment effects (Difference-in-Differences (DiD), causal machine learning), uncertainty and sensitivity analysis (bootstrap, Monte Carlo, Bayesian), and automated model selection and performance comparison. Designed for long-term datasets covering soil, water, crop, and climate domains. Key references: Mann and Kendall (1945) <doi:10.2307/1907187>; Sen (1968) <doi:10.1080/01621459.1968.10480934>; Bai and Perron (2003) <doi:10.1002/jae.659>; Rosseel (2012) <doi:10.18637/jss.v048.i02>; Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>.

This package provides Scilab n1qn1'. This takes more memory than traditional L-BFGS. The n1qn1 routine is useful since it allows prespecification of a Hessian. If the Hessian is near enough the truth in optimization it can speed up the optimization problem. The algorithm is described in the Scilab optimization documentation located at <https://www.scilab.org/sites/default/files/optimization_in_scilab.pdf>. This version uses manually modified code from f2c to make this a C only binary.

This package provides functions for manipulating nested data frames in a list-column using dplyr <https://dplyr.tidyverse.org/> syntax. Rather than unnesting, then manipulating a data frame, nplyr allows users to manipulate each nested data frame directly. nplyr is a wrapper for dplyr functions that provide tools for common data manipulation steps: filtering rows, selecting columns, summarising grouped data, among others.

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Fast and extensible framework for discovering interesting patterns in tabular data. The package searches combinations of fuzzy or Boolean predicates and evaluates the resulting subgroups using statistical, logical, or structural measures. It supports a broad range of pattern-discovery tasks, including association rules (Agrawal et al., 1994, <https://www.vldb.org/conf/1994/P487.PDF>), contrast patterns (Chen, 2022, <doi:10.48550/arXiv.2209.13556>), emerging patterns (Dong et al., 1999, <doi:10.1145/312129.312191>), subgroup discovery (Atzmueller, 2015, <doi:10.1002/widm.1144>), and conditional correlations (Hájek, 1978, <doi:10.1007/978-3-642-66943-9>). User-defined functions may be supplied to guide custom pattern searches, making the framework applicable beyond traditional association-rule mining. Efficient implementation enables pattern discovery on large and dense data sets. Package includes methods for visualization and supports interactive exploration through integrated Shiny applications.

This package provides a small package designed for interpreting continuous and categorical latent variables. You provide a data set with a latent variable you want to understand and some other explanatory variables. It provides a description of the latent variable based on the explanatory variables. It also provides a name to the latent variable.

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.

This package provides a nested menu widget for usage in Shiny applications. This is useful for hierarchical choices (e.g. continent, country, city).

This package contains functions useful for debugging, set operations on vectors, and UTC date and time functionality. It adds a few vector manipulation verbs to purrr and dplyr packages. It can also generate an R file to install and update packages to simplify deployment into production. The functions were developed at the data science firm Numeract LLC and are used in several packages and projects.

This package provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon igraph'.