Apply neutrosophic regression type estimator and performs neutrosophic interval analysis including metric calculations for survey data.

An interactive document on the topic of naive Bayes classification analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/NBShiny/>.

Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: <https://github.com/sduxbury/netmediate>. BugReports: <https://github.com/sduxbury/netmediate/issues>. Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>. Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>. Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>. Duxbury, Scott (2023) <doi:10.1177/00811750231209040>. Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.

Calculate various functions needed for design and monitoring clinical trials with negative binomial endpoint with variable follow-up. This version has a few changes compared to the previous version 1.0.0, including (1) correct a typo in Type 1 censoring, mtbnull=bnull and (2) restructure the code to account for shape parameter equal to zero, i.e. Poisson scenario.

This package provides functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.

Cross-Entropy optimisation of unconstrained deterministic and noisy functions illustrated in Rubinstein and Kroese (2004, ISBN: 978-1-4419-1940-3) through a highly flexible and customisable function which allows user to define custom variable domains, sampling distributions, updating and smoothing rules, and stopping criteria. Several built-in methods and settings make the package very easy-to-use under standard optimisation problems.

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

Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN.

Downloads and reads data from Human Connectome Project <https://db.humanconnectome.org> using Amazon Web Services ('AWS') S3 buckets.

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.

Build and run spatially explicit agent-based models using only the R platform. NetLogoR follows the same framework as the NetLogo software (Wilensky (1999) <https://www.netlogo.org>) and is a translation in R of the structure and functions of NetLogo'. NetLogoR provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants <https://ccl.northwestern.edu/netlogo/models/Ants>) and Wolf-Sheep-Predation (<https://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>), and a third, Butterfly, from Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all written using NetLogoR are available. The NetLogo code of the original version of these models is provided alongside. A programming guide inspired from the NetLogo Programming Guide (<https://docs.netlogo.org/programming.html>) and a dictionary of NetLogo primitives (<https://docs.netlogo.org/dictionary.html>) equivalences are also available. NOTE: To increment time', these functions can use a for loop or can be integrated with a discrete event simulator, such as SpaDES (<https://cran.r-project.org/package=SpaDES>).

An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.

Nonnegative matrix factorization (NMF) is a technique to factorize a matrix with nonnegative values into the product of two matrices. Covariates are also allowed. Parallel computing is an option to enhance the speed and high-dimensional and large scale (and/or sparse) data are allowed. Relevant papers include: Wang Y. X. and Zhang Y. J. (2012). Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering, 25(6): 1336-1353 <doi:10.1109/TKDE.2012.51> and Kim H. and Park H. (2008). Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM Journal on Matrix Analysis and Applications, 30(2): 713-730 <doi:10.1137/07069239X>.

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at <doi:10.1111/biom.13857>.

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (i.e., the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, alpha, on the type I error. Although the NP paradigm has a century-long history in hypothesis testing, it has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than alpha do not satisfy the type I error control objective because the resulting classifiers are still likely to have type I errors much larger than alpha. As a result, the NP paradigm has not been properly implemented for many classification scenarios in practice. In this work, we develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, including popular methods such as logistic regression, support vector machines and random forests. Powered by this umbrella algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands, motivated by the popular receiver operating characteristic (ROC) curves. NP-ROC bands will help choose in a data adaptive way and compare different NP classifiers.

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. NGCHM allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

Designed to add datasets which are used in the Nonparametric Statistical Methods textbook, 3rd edition.

This package contains the following 5 nonparametric hypothesis tests: The Sign Test, The 2 Sample Median Test, Miller's Jackknife Procedure, Cochran's Q Test, & The Stuart-Maxwell Test.

This package provides a navigation menu to enable pipe-friendly data processing for hierarchical data structures. By activating the menu items, you can perform operations on each item while maintaining the overall structure in attributes.

This package provides a nonvisual procedure for screening time series for nonstationarity in the context of intensive longitudinal designs, such as ecological momentary assessments. The method combines two diagnostics: one for detecting trends (based on the split R-hat statistic from Bayesian convergence diagnostics) and one for detecting changes in variance (a novel extension inspired by Levene's test). This approach allows researchers to efficiently and reproducibly detect violations of the stationarity assumption, especially when visual inspection of many individual time series is impractical. The procedure is suitable for use in all areas of research where time series analysis is central. For a detailed description of the method and its validation through simulations and empirical application, see Zitzmann, S., Lindner, C., Lohmann, J. F., & Hecht, M. (2024) "A Novel Nonvisual Procedure for Screening for Nonstationarity in Time Series as Obtained from Intensive Longitudinal Designs" <https://www.researchgate.net/publication/384354932_A_Novel_Nonvisual_Procedure_for_Screening_for_Nonstationarity_in_Time_Series_as_Obtained_from_Intensive_Longitudinal_Designs>.

This package performs nonparametric analysis of longitudinal data in factorial experiments. Longitudinal data are those which are collected from the same subjects over time, and they frequently arise in biological sciences. Nonparametric methods do not require distributional assumptions, and are applicable to a variety of data types (continuous, discrete, purely ordinal, and dichotomous). Such methods are also robust with respect to outliers and for small sample sizes.

Factorize binary matrices into rank-k components using the logistic function in the updating process. See e.g. Tomé et al (2015) <doi:10.1007/s11045-013-0240-9> .

Wraps the nametag library <https://github.com/ufal/nametag>, allowing users to find and extract entities (names, persons, locations, addresses, ...) in raw text and build your own entity recognition models. Based on a maximum entropy Markov model which is described in Strakova J., Straka M. and Hajic J. (2013) <https://ufal.mff.cuni.cz/~straka/papers/2013-tsd_ner.pdf>.