Makes NCBI taxonomic data locally available and searchable as an R object.

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

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>.

This package provides tools for analyzing spatial data, especially non- Gaussian areal data. The current version supports the sparse restricted spatial regression model of Hughes and Haran (2013) <DOI:10.1111/j.1467-9868.2012.01041.x>, the centered autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>, and the Bayesian spatial filtering model of Hughes (2017) <arXiv:1706.04651>.

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.

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.

Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes. Fitting models to data using MLE (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the LDL (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.

This package provides a set of functions to simulate National Football League seasons including the sophisticated tie-breaking procedures.

Optimization for nonlinear objective and constraint functions. Linear or nonlinear equality and inequality constraints are allowed. It accepts the input parameters as a constrained matrix.

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>).

An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.

This R package provides a calculation of between-cases AUC estimate, corresponding covariance, and variance estimate in the nested data problem. Also, the package has the function to simulate the nested data. The calculated between-cases AUC estimate is used to evaluate the reader's diagnostic performance in clinical tasks with nested data. For more details on the above methods, please refer to the paper by H Du, S Wen, Y Guo, F Jin, BD Gallas (2022) <doi:10.1177/09622802221111539>.

Instant access to harmonized National Health and Nutrition Examination Survey (NHANES) data spanning 1999-2023. Retrieve pre-processed datasets from reliable cloud storage with automatic type reconciliation and integrated search tools for variables and datasets. Simplifies NHANES data workflows by handling cycle management and maintaining data consistency across survey waves. Data is sourced from <https://www.cdc.gov/nchs/nhanes/>.

Given any graph, the node2vec algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.

Robust nonparametric bootstrap and permutation tests for goodness of fit, distribution equivalence, location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the parallel package.

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

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.

Counts syllables in character vectors for English words. Imputes syllables as the number of vowel sequences for words not found.

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>).

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.

This package provides a simple function for easier package loading and auto-installation.

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>.

Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>. Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.

Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.