This package provides several direct search optimization algorithms based on the simplex method. The provided algorithms are direct search algorithms, i.e. algorithms which do not use the derivative of the cost function. They are based on the update of a simplex. The following algorithms are available: the fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained optimization with a fixed shape simplex, 1962) <doi:10.1080/00401706.1962.10490033>, the variable shape simplex method of Nelder and Mead (unconstrained optimization with a variable shape simplex made, 1965) <doi:10.1093/comjnl/7.4.308>, and Box's complex method (constrained optimization with a variable shape simplex, 1965) <doi: 10.1093/comjnl/8.1.42>.

Extends package nat (NeuroAnatomy Toolbox) by providing a collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).

This package infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.

Data sets and nonlinear regression models dedicated to predictive microbiology.

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>.

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

This package implements a four-stage pipeline for probabilistic seismic performance analysis of slopes and embankments. The package takes a uniform-hazard spectrum at multiple return periods as input (any source) and produces: (1) synthetic soil profile generation and fundamental period estimation from USCS classification via Ishihara's small-strain shear-modulus model and the inhomogeneous truncated shear-beam theory of Gazetas and Dakoulas; (2) nonlinear site amplification using the Seyhan & Stewart (2014) model <doi:10.1193/063013EQS181M>, with inter-period correlation via Baker & Jayaram (2008) <doi:10.1193/1.2857544>; (3) Monte Carlo ensemble of six empirical Newmark sliding-block displacement models (Ambraseys & Menu (1988) <doi:10.1002/eqe.4290160704>, Jibson (2007) <doi:10.1016/j.enggeo.2007.01.013>, Saygili & Rathje (2008) <doi:10.1061/(ASCE)1090-0241(2008)134:6(790)>, Bray & Travasarou (2007) <doi:10.1061/(ASCE)1090-0241(2007)133:4(381)>, Bray & Macedo (2017) <doi:10.1016/j.soildyn.2017.05.024>, and the Bray and Macedo shallow-crustal update) with coherent correlated draws; (4) log-log inversion to the performance-based seismic coefficient kmax at user-specified displacement targets. All outputs are data.table objects.

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

Box-constrained multiobjective optimization using the elitist non-dominated sorting genetic algorithm - NSGA-II. Fast non-dominated sorting, crowding distance, tournament selection, simulated binary crossover, and polynomial mutation are called in the main program. The methods are described in Deb et al. (2002) <doi:10.1109/4235.996017>.

This package provides a nomogram can not be easily applied, because it is difficult to calculate the points or even the survival probability. The package, including a function of nomogramEx(), is to extract the polynomial equations to calculate the points of each variable, and the survival probability corresponding to the total points.

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

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/>.

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.

This package provides a collection of colour palettes derived from photographs of nudis and sea slugs I have encountered in intertidal zones and shallow rocky reefs around Sydney, Australia. Palettes can be used in base R or with ggplot2'.

NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and Amira AmiraMesh formats and reads surfaces in Amira hxsurf format. Traced neurons can be imported from and written to SWC and Amira LineSet and SkeletonGraph formats. These data can then be visualised in 3D via rgl', manipulated including applying calculated registrations, e.g. using the CMTK registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the nat.nblast extension package).

Segmentation of short text sequences - like hashtags - into the separated words sequence, done with the use of dictionary, which may be built on custom corpus of texts. Unigram dictionary is used to find most probable sequence, and n-grams approach is used to determine possible segmentation given the text corpus.

R interface for the netstat command line utility used to retrieve and parse commonly used network statistics, including available and in-use transmission control protocol (TCP) ports. Primers offering technical background information on the netstat command line utility are available in the "Linux System Administrator's Manual" by Michael Kerrisk (2014) <https://man7.org/linux/man-pages/man8/netstat.8.html>, and on the Microsoft website (2017) <https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/netstat>.

Several methods have been developed to integrate structural equation modeling techniques with network data analysis to examine the relationship between network and non-network data. Both node-based and edge-based information can be extracted from the network data to be used as observed variables in structural equation modeling. To facilitate the application of these methods, model specification can be performed in the familiar syntax of the lavaan package, ensuring ease of use for researchers. Technical details and examples can be found at <https://bigsem.psychstat.org>.

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>.

Native rasters are a core R image format which use a compact color representation. This color representation closely aligns with graphics device internals meaning that these images can be rendered quickly. This package provides functions to quickly create, manipulate and composite native rasters.

Datasets for testing nonlinear regression routines.

This package performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.

Ships statistical and mathematical routines from R internal nmath ('Mathlib') as OpenCL C sources under directory inst/cl/', with R wrappers that use the GPU when OpenCL is available at compile time and fall back to stats equivalents otherwise. Aimed at package developers building custom kernels (for example Bayesian GLMs via suggested package glmbayes') using opencltools kernel loaders and related helpers. Contains translated shims, an illustrative GLM-related kernel subsystem, vignettes, and optional GPU acceleration. The ported routines are translated from the nmath ('Mathlib') and Rmath sources of R Core Team (2026) "R: A Language and Environment for Statistical Computing" <doi:10.32614/R.manuals>. OpenCL GPU execution follows the standard described in Stone, Gohara, and Shi (2010) <doi:10.1109/MCSE.2010.69>. The likelihood subgradient simulation methodology implemented by the illustrative GLM kernel subsystem is described in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>.

The number of distinct alleles observed in a DNA mixture is informative of the number of contributors to the mixture. The package provides methods for computing the probability distribution of the number of distinct alleles in a mixture for a given set of allele frequencies. The mixture contributors may be related according to a provided pedigree.