Draw samples from truncated multivariate normal distribution using the sequential nearest neighbor (SNN) method introduced in "Scalable Sampling of Truncated Multivariate Normals Using Sequential Nearest-Neighbor Approximation" <doi:10.48550/arXiv.2406.17307>.

This package provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHS Making Data Count programme, and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

This package provides a novel integral estimator for estimating the causal effects with continuous treatments (or dose-response curves) and a localized derivative estimator for estimating the derivative effects. The inference on the dose-response curve and its derivative is conducted via nonparametric bootstrap. The reference paper is Zhang, Chen, and Giessing (2024) <doi:10.48550/arXiv.2405.09003>.

The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the best fit on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.

Model-based clustering of high-dimensional non-negative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. Correlation between variables are allowed, while samples are assumed to be independent.

Represent network or igraph objects whose vertices can be represented by features in an sf object as a network graph surmising a sf plot. Fits into ggplot2 grammar.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). This package is for support functions like preconditioned fits <doi:10.1208/s12248-016-9866-5>, boostrap and stepwise covariate selection.

This package provides functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

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

Computes the nonlinear cointegrating autoregressive distributed lag model with automatic bases aic and bic lags selection of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).

In this implementation of the Naive Bayes classifier following class conditional distributions are available: Bernoulli', Categorical', Gaussian', Poisson', Multinomial and non-parametric representation of the class conditional density estimated via Kernel Density Estimation. Implemented classifiers handle missing data and can take advantage of sparse data.

This package provides functions complementary to packages nicheROVER and SIBER allowing the user to extract Bayesian estimates from data objects created by the packages nicheROVER and SIBER'. Please see the following publications for detailed methods on nicheROVER and SIBER Hansen et al. (2015) <doi:10.1890/14-0235.1>, Jackson et al. (2011) <do i:10.1111/j.1365-2656.2011.01806.x>, and Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>, respectfully.

This package performs nonlinear Invariant Causal Prediction to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending Invariant Causal Prediction from Peters, Buehlmann and Meinshausen (2016), <arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml, J. Peters and N. Meinshausen: Invariant Causal Prediction for Nonlinear Models', <arXiv:1706.08576>.

Catalogue of NBER working papers published between June 1973 and December 2021.

This package implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) <doi:10.1007/s00184-008-0171-x>. The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests.

This package provides functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

The Nordklim dataset 1.0 is a unique and useful achievement for climate analysis. It includes observations of twelve different climate elements from more than 100 stations in the Nordic region, in time span over 100 years. The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI).

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>.

Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of JavaScript in one package.

Closed testing has been proved powerful for true discovery guarantee. The computation of closed testing is, however, quite burdensome. A general way to reduce computational complexity is to combine partial closed testings for some prespecified feature sets of interest. Partial closed testings are performed at Bonferroni-corrected alpha level to guarantee the lower bounds for the number of true discoveries in prespecified sets are simultaneously valid. For any post hoc chosen sets of interest, coherence property is used to get the lower bound. In this package, we implement closed testing with globaltest to calculate the lower bound for number of true discoveries, see Ningning Xu et.al (2021) <arXiv:2001.01541> for detailed description.

This package provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.

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 comprehensive toolkit for calculating and visualizing Nitrogen Use Efficiency (NUE) indicators in agricultural research. The package implements 23 parameters categorized into fertilizer-based, plant-based, soil-based, isotope-based, ecology-based, and system-based indicators based on Congreves et al. (2021) <doi:10.3389/fpls.2021.637108>. Key features include vectorized calculations for paired-plot experimental designs, batch processing capabilities for handling large datasets, and built-in visualization tools using ggplot2'. Designed to streamline the workflow from raw agronomic data to publication-ready metrics and plots.