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.

Simulates events from one dimensional nonhomogeneous Poisson point processes (NHPPPs) as per Trikalinos and Sereda (2024, <doi:10.48550/arXiv.2402.00358> and 2024, <doi:10.1371/journal.pone.0311311>). Functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function (Cinlar E, "Theory of stochastic processes" (1975, ISBN:0486497996)); the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme (Lewis PAW, Shedler, GS (1979) <doi:10.1002/nav.3800260304>).

Calculates a cumulative summation nonparametric extended median test based on the work of Brown & Schaffer (2020) <DOI:10.1080/03610926.2020.1738492>. It then generates a control chart to assess processes and determine if any streams are out of control.

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.

An adaptation of Non-dominated Sorting Genetic Algorithm III for multi objective feature selection tasks. Non-dominated Sorting Genetic Algorithm III is a genetic algorithm that solves multiple optimization problems simultaneously by applying a non-dominated sorting technique. It uses a reference points based selection operator to explore solution space and preserve diversity. See the original paper by K. Deb and H. Jain (2014) <DOI:10.1109/TEVC.2013.2281534> for a detailed description.

The implementation of Markov Model Multiple Imputation with the application to COVID-19 scale, NIAID OS.

This package provides functions for nominal data mining based on bipartite graphs, which build a pipeline for analysis and missing values imputation. Methods are mainly from the paper: Jafari, Mohieddin, et al. (2021) <doi:10.1101/2021.03.18.436040>, some new ones are also included.

Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.

The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.

Snow water equivalent is modeled with the process based models delta.snow and HS2SWE and empirical regression, which use relationships between density and diverse at-site parameters. The methods are described in Winkler et al. (2021) <doi:10.5194/hess-25-1165-2021>, Magnusson et al. (2025) <doi:10.1016/j.coldregions.2025.104435>, Guyennon et al. (2019) <doi:10.1016/j.coldregions.2019.102859>, Pistocchi (2016) <doi:10.1016/j.ejrh.2016.03.004>, Jonas et al. (2009) <doi:10.1016/j.jhydrol.2009.09.021> and Sturm et al. (2010) <doi:10.1175/2010JHM1202.1>.

This package provides functions for the normal Laplace distribution. Currently, it provides limited functionality. Density, distribution and quantile functions, random number generation, and moments are provided.

This package provides a toolbox for continuous norming of psychological and educational tests, supporting regression-based norming where norms can vary as a continuous function of age or another norm predictor. Norms are estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), enabling flexible modelling of the full score distribution in a normative sample. The package supports applications in psychometrics and psychological testing, and includes functions for model selection, reliability estimation, norm calculation, including confidence intervals, and sample size planning. For more details, see Timmerman et al. (2021) <doi:10.1037/met0000348>.

Calculating the density, cumulative distribution, quantile, and random number of neo-normal distribution. It also interfaces with the brms package, allowing the use of the neo-normal distribution as a custom family. This integration enables the application of various brms formulas for neo-normal regression. Modified to be Stable as Normal from Burr (MSNBurr), Modified to be Stable as Normal from Burr-IIa (MSNBurr-IIa), Generalized of MSNBurr (GMSNBurr), Jones-Faddy Skew-t, Fernandez-Osiewalski-Steel Skew Exponential Power, and Jones Skew Exponential Power distributions are supported. References: Choir, A. S. (2020).Unpublished Dissertation, Iriawan, N. (2000).Unpublished Dissertation, Rigby, R. A., Stasinopoulos, M. D., Heller, G. Z., & Bastiani, F. D. (2019) <doi:10.1201/9780429298547>.

Palettes generated from NBA jersey colorways.

Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. The package implements various methods such as inverse probability (propensity score) weighting, mass imputation and doubly robust approach. Details can be found in: Chen et al. (2020) <doi:10.1080/01621459.2019.1677241>, Yang et al. (2020) <doi:10.1111/rssb.12354>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Yang et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm> and Wu (2022) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>. For details on the package and its functionalities see <doi:10.48550/arXiv.2504.04255>.

This package provides a collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>, Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>, Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>, Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>, Barve et al. (2011) <doi:10.1111/ecog.02671>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and Cobos et al. (2021) <doi:10.1111/jav.02868>.

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'.

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/>.

This package implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.

NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. NACHO (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. NACHO does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Analysis of multivariate data with two-way completely randomized factorial design. The analysis is based on fully nonparametric, rank-based methods and uses test statistics based on the Dempster's ANOVA, Wilk's Lambda, Lawley-Hotelling and Bartlett-Nanda-Pillai criteria. The multivariate response is allowed to be ordinal, quantitative, binary or a mixture of the different variable types. The package offers two functions performing the analysis, one for small and the other for large sample sizes. The underlying methodology is largely described in Bathke and Harrar (2016) <doi:10.1007/978-3-319-39065-9_7> and in Munzel and Brunner (2000) <doi:10.1016/S0378-3758(99)00212-8> and in Kiefel and Bathke (2022) <doi:10.1515/stat-2022-0112>.

This package provides a computational toolkit for analyzing nematode communities in ecological studies. Includes methods to quantify nematode-based ecological indicators such as metabolic footprints, energy flow metrics, and community structure. These tools support assessments of soil health, ecosystem functioning, and trophic interactions, standardizing the use of nematodes as bioindicators.

This package implements the routines to compare the survival curves with recurrent events, including the estimations of survival curves. The first model is a model for recurrent event, when the data are correlated or not correlated. It was proposed by Wang and Chang (1999) <doi:10.2307/2669690>. In the independent case, the survival function can be estimated by the generalization of the limit product model of Pena (2001) <doi:10.1198/016214501753381922>.