This package provides functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.

Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.

Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are best revealed by using Nonparametric and Unsupervised methods to "Design" the analysis of the given data ...rather than the collection of "designed data". Specifically, the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and either a binary t-Treatment or continuous e-Exposure variable needs to consist of BLOCKS of relatively well-matched experimental units (e.g. patients) that have the most similar X-confounder characteristics. Since our NU Learning approach will form BLOCKS by "clustering" experimental units in confounder X-space, the implicit statistical model for learning is One-Way ANOVA. Within Block measures of effect-size are then either [a] LOCAL Treatment Differences (LTDs) between Within-Cluster y-Outcome Means ("new" minus "control") when treatment choice is Binary or else [b] LOCAL Rank Correlations (LRCs) when the e-Exposure variable is numeric with (hopefully many) more than two levels. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences when X-Covariates are assumed to influence Treatment choice or Exposure level but otherwise have no direct effects on y-Outcomes. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient (or Researcher-Society) communications about Heterogeneous Outcomes. Obenchain and Young (2013) <doi:10.1080/15598608.2013.772821>; Obenchain, Young and Krstic (2019) <doi:10.1016/j.yrtph.2019.104418>.

This package performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

This package implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.

Estimate the non-linear odds ratio and plot it against a continuous exposure.

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/>.

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>.

This package provides functions and datasets to support the book by Galecki and Burzykowski (2013), Linear Mixed-Effects Models: A Step-by-Step Approach', Springer. Includes functions for power calculations, log-likelihood contributions, and data simulation for linear mixed-effects models.

Different inference procedures are proposed in the literature to correct for selection bias that might be introduced with non-random selection mechanisms. A class of methods to correct for selection bias is to apply a statistical model to predict the units not in the sample (super-population modeling). Other studies use calibration or Statistical Matching (statistically match nonprobability and probability samples). To date, the more relevant methods are weighting by Propensity Score Adjustment (PSA). The Propensity Score Adjustment method was originally developed to construct weights by estimating response probabilities and using them in Horvitzâ Thompson type estimators. This method is usually used by combining a non-probability sample with a reference sample to construct propensity models for the non-probability sample. Calibration can be used in a posterior way to adding information of auxiliary variables. Propensity scores in PSA are usually estimated using logistic regression models. Machine learning classification algorithms can be used as alternatives for logistic regression as a technique to estimate propensities. The package NonProbEst implements some of these methods and thus provides a wide options to work with data coming from a non-probabilistic sample.

Do algebraic operations on neural networks. We seek here to implement in R, operations on neural networks and their resulting approximations. Our operations derive their descriptions mainly from Rafi S., Padgett, J.L., and Nakarmi, U. (2024), "Towards an Algebraic Framework For Approximating Functions Using Neural Network Polynomials", <doi:10.48550/arXiv.2402.01058>, Grohs P., Hornung, F., Jentzen, A. et al. (2023), "Space-time error estimates for deep neural network approximations for differential equations", <doi:10.1007/s10444-022-09970-2>, Jentzen A., Kuckuck B., von Wurstemberger, P. (2023), "Mathematical Introduction to Deep Learning Methods, Implementations, and Theory" <doi:10.48550/arXiv.2310.20360>. Our implementation is meant mainly as a pedagogical tool, and proof of concept. Faster implementations with deeper vectorizations may be made in future versions.

This package provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.

Linear regression model and generalized linear models with nonparametric network effects on network-linked observations. The model is originally proposed by Le and Li (2022) <doi:10.48550/arXiv.2007.00803> and is assumed on observations that are connected by a network or similar relational data structure. A more recent work by Wang, Le and Li (2024) <doi:10.48550/arXiv.2410.01163> further extends the framework to generalized linear models. All these models are implemented in the current package. The model does not assume that the relational data or network structure to be precisely observed; thus, the method is provably robust to a certain level of perturbation of the network structure. The package contains the estimation and inference function for the model.

An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped.

To study network evolution models and different blockmodeling approaches. Various functions enable generating (temporal) networks with a selected blockmodel type, taking into account selected local network mechanisms. The development of this package is financially supported the Slovenian Research Agency (www.arrs.gov.si) within the research program P5<96>0168 and the research project J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Social network analysis has become an essential tool in the study of complex systems. NetExplorer allows to visualize and explore complex systems. It is based on d3js library that brings 1) Graphical user interface; 2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.

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 provides a Software Development Kit for working with Nixtla''s TimeGPT', a foundation model for time series forecasting. API is an acronym for application programming interface'; this package allows users to interact with TimeGPT via the API'. You can set and validate API keys and generate forecasts via API calls. It is compatible with tsibble and base R. For more details visit <https://docs.nixtla.io/>.

Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.

This package performs variable selection in sparse negative binomial GLARMA (Generalised Linear Autoregressive Moving Average) models. For further details we refer the reader to the paper Gomtsyan (2023), <arXiv:2307.00929>.

Multiple and generalized nonparametric regression using smoothing spline ANOVA models and generalized additive models, as described in Helwig (2020) <doi:10.4135/9781526421036885885>. Includes support for Gaussian and non-Gaussian responses, smoothers for multiple types of predictors (including random intercepts), interactions between smoothers of mixed types, eight different methods for smoothing parameter selection, and flexible tools for diagnostics, inference, and prediction.

Extends the classical Newman studentized range statistic in various ways that can be applied to genome-scale transcriptomic or other expression data.

Interface to the Nomis database (<https://www.nomisweb.co.uk>), a comprehensive resource of United Kingdom labour market statistics provided by the Office for National Statistics (ONS). Facilitates programmatic access to census data, labour force surveys, benefit statistics, and socioeconomic indicators through a modern HTTP client with intelligent caching, automatic query pagination, and tidy data principles. Includes spatial data integration, interactive helpers, and visualization utilities. Independent implementation unaffiliated with ONS or Durham University.

Optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, dose rate determination, growth curve fitting, decay curve decomposition, statistical age model optimization, and statistical plot visualization.