Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

This is a cross-platform linear model to SQL compiler. It generates SQL from linear and generalized linear models. Its interface consists of a single function, modelc(), which takes the output of lm() or glm() functions (or any object which has the same signature) and outputs a SQL character vector representing the predictions on the scale of the response variable as described in Dunn & Smith (2018) <doi:10.1007/978-1-4419-0118-7> and originating in Nelder & Wedderburn (1972) <doi:10.2307/2344614>. The resultant SQL can be included in a SELECT statement and returns output similar to that of the glm.predict() or lm.predict() predictions, assuming numeric types are represented in the database using sufficient precision. Currently log and identity link functions are supported.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

This package provides a container for data used by the mapindia package. The data used by mapindia has been extracted into this package so that the file size of the mapindia package can be reduced considerably. The data in this package will be updated when latest data is available.

This package provides a comprehensive framework for analyzing agricultural nutrient balances across multiple spatial scales (county, HUC8', HUC2') with integration of wastewater treatment plant ('WWTP') effluent loads for both nitrogen and phosphorus. Supports classification of spatial units as nutrient sources, sinks, or balanced areas based on agricultural surplus and deficit calculations. Includes visualization tools, spatial transition probability analysis, and nutrient flow network mapping. Built-in datasets include agricultural nutrient balance data from the Nutrient Use Geographic Information System ('NuGIS'; The Fertilizer Institute and Plant Nutrition Canada, 1987-2016) <https://nugis.tfi.org/tabular_data/> and U.S. Environmental Protection Agency ('EPA') wastewater discharge data from the ECHO Discharge Monitoring Report ('DMR') Loading Tool (2007-2016) <https://echo.epa.gov/trends/loading-tool/water-pollution-search>. Data are downloaded on demand from the Open Science Framework ('OSF') repository to minimize package size while maintaining full functionality. The integrated manureshed framework methodology is described in Akanbi et al. (2025) <doi:10.1016/j.resconrec.2025.108697>. Designed for nutrient management planning, environmental analysis, and circular economy research at watershed/administrative to national scales. This material is based upon financial support by the National Science Foundation, EEC Division of Engineering Education and Centers, NSF Engineering Research Center for Advancing Sustainable and Distributed Fertilizer Production (CASFER), NSF 20-553 Gen-4 Engineering Research Centers award 2133576. We thank Dr. Robert D. Sabo (U.S. Environmental Protection Agency) for his valuable contributions to the conceptual development and review of this work.

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>.

An R-package for calculating sample size of a survival trial with or without cure fractions.

This package provides a cross-platform interface to prevent the operating system from going to sleep while long-running R tasks are executing.

This package provides a Non-Metric Space Library ('NMSLIB <https://github.com/nmslib/nmslib>) wrapper, which according to the authors "is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the NMSLIB <https://github.com/nmslib/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods". The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the NMSLIB <https://github.com/nmslib/nmslib> Python Library.

Neighbour-balanced designs ensure that no treatment is disadvantaged unfairly by its surroundings. The treatment allocation in these designs is such that every treatment appears equally often as a neighbour with every other treatment. Neighbour Balanced Designs are employed when there is a possibility of neighbour effects from treatments used in adjacent experimental units. In the literature, a vast number of such designs have been developed. This package generates some efficient neighbour balanced block designs which are balanced and partially variance balanced for estimating the contrast pertaining to direct and neighbour effects, as well as provides a function for analysing the data obtained from such trials (Azais, J.M., Bailey, R.A. and Monod, H. (1993). "A catalogue of efficient neighbour designs with border plots". Biometrics, 49, 1252-1261 ; Tomar, J. S., Jaggi, Seema and Varghese, Cini (2005)<DOI: 10.1080/0266476042000305177>. "On totally balanced block designs for competition effects"). This package contains functions named nbbd1(),nbbd2(),nbbd3(),pnbbd1() and pnbbd2() which generates neighbour balanced block designs within a specified range of number of treatment (v). It contains another function named anlys()for performing the analysis of data generated from such trials.

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>.

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

Simulation and estimation for Neyman-Scott spatial cluster point process models and their extensions, based on the methodology in Tanaka, Ogata, and Stoyan (2008) <doi:10.1002/bimj.200610339>. To estimate parameters by the simplex method, parallel computation using OpenMP application programming interface is available. For more details see Tanaka, Saga and Nakano <doi:10.18637/jss.v098.i06>.

Nonparametric Failure Time (NFT) Bayesian Additive Regression Trees (BART): Time-to-event Machine Learning with Heteroskedastic Bayesian Additive Regression Trees (HBART) and Low Information Omnibus (LIO) Dirichlet Process Mixtures (DPM). An NFT BART model is of the form Y = mu + f(x) + sd(x) E where functions f and sd have BART and HBART priors, respectively, while E is a nonparametric error distribution due to a DPM LIO prior hierarchy. See the following for a description of the model at <doi:10.1111/biom.13857>.

Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME). URL: <https://github.com/sduxbury/netmediate>. BugReports: <https://github.com/sduxbury/netmediate/issues>. Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>. Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>. Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>. Duxbury, Scott (2023) <doi:10.1177/00811750231209040>. Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.

This package provides tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Set of functions implementing the algorithm described in Fernandez Torvisco et al. (2018) for fitting separable nonlinear regression curves. See Fernandez Torvisco, Rodriguez-Arias Fernandez and Cabello Sanchez (2018) <doi:10.2298/FIL1812233T>.

Retrieve and plot word frequencies through time from the "Google Ngram Viewer" <https://books.google.com/ngrams>.

Classification, regression, and clustering with k nearest neighbors algorithm. Implements several distance and similarity measures, covering continuous and logical features. Outputs ranked neighbors. Most features of this package are directly based on the PMML specification for KNN.

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

Calculates network measures commonly used in Network Medicine. Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving.

In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of ivregress in Stata with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.

Perform an exploration and a preliminary analysis on the dose- response relationship of nanomaterial toxicity. Several functions are provided for data exploration, including functions for creating a subset of dataset, frequency tables and plots. Inference for order restricted dose- response data is performed by testing the significance of monotonic dose-response relationship, using Williams, Marcus, M, Modified M and Likelihood ratio tests. Several methods of multiplicity adjustment are also provided. Description of the methods can be found in <https://github.com/rahmasarina/dose-response-analysis/blob/main/Methodology.pdf>.

This is an R implementation of the netinf algorithm (Gomez Rodriguez, Leskovec, and Krause, 2010)<doi:10.1145/1835804.1835933>. Given a set of events that spread between a set of nodes the algorithm infers the most likely stable diffusion network that is underlying the diffusion process.