Distance between multivariate t distributions, as presented by N. Bouhlel and D. Rousseau (2023) <doi:10.1109/LSP.2023.3324594>.

Routines to generate fully randomized moodle quizzes. It also contains 15 examples and a shiny app.

Supports the full analysis pipeline for researchers working with firm-level microdata. Provides data tools for panel preparation (import, outlier detection, classification harmonization), analytical methods (production function estimation, capital stock measurement, markups, intensity measures, distributions, regression, clustering), and disclosure tools for tagging outputs with dominance and observation counts before aggregation and publication. Production function estimation implements methods by Ackerberg, Caves and Frazer (2015) <doi:10.3982/ECTA13408>, Levinsohn and Petrin (2003) <doi:10.1111/1467-937X.00246>, Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>, Petrin, Poi and Levinsohn (2004) <doi:10.1177/1536867X0400400202>, and Arellano and Bond (1991) <doi:10.2307/2297968> with the "too many instruments" correction by Roodman (2009) <doi:10.1111/j.1468-0084.2008.00542.x>. Markup estimation follows De Loecker and Warzynski (2012) <doi:10.1257/aer.102.6.2437>. Cost-share production function estimation follows Basu and Fernald (1997) <doi:10.1086/262073>. Capital stock estimation via the Perpetual Inventory Method follows OECD (2009) <doi:10.1787/9789264068476-en>.

Fast masked KNN imputation for tabular data with support for single and multiple imputation.

This package implements bivariate and Multivariate Quantile-on-Quantile Granger causality tests building on the Quantile-on-Quantile regression framework of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger causality test of Troster (2018) <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates the local-linear slope in the quantile regression of y_t on lagged x_t with lagged y_t as control, using Gaussian kernel weights, and tests it against zero by paired bootstrap. The multivariate (conditional) test additionally conditions on a set of moderators Z and optional x times Z interaction terms, in the spirit of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the quantile grid is also provided. Heatmaps and 3D surfaces default to the MATLAB Parula colour map.

Fits matrix-variate variance-gamma (MVVG) and matrix-variate normal-inverse-Gaussian (MVNIG) linear regression models using expectation-conditional maximization (ECM) algorithms. The models accommodate clustered matrix-valued responses, with unequal numbers of observations across subjects, correlated responses, skewness, and within-subject dependence. Functions are provided for model fitting, prediction, and subject-level influence assessment using approximate generalized Cook's distances. The package also includes motivating periodontal data from Gullah-speaking African Americans with Type-II diabetes. For details on the underlying matrix-variate distributions (MVVG and MVNIG), see Gallaugher and McNicholas (2019, <doi:10.1016/j.spl.2018.08.012>).

Supply functions for the creation and handling of missing data as well as tools to evaluate missing data methods. Nearly all possibilities of generating missing data discussed by Santos et al. (2019) <doi:10.1109/ACCESS.2019.2891360> and some additional are implemented. Functions are supplied to compare parameter estimates and imputed values to true values to evaluate missing data methods. Evaluations of these types are done, for example, by Cetin-Berber et al. (2019) <doi:10.1177/0013164418805532> and Kim et al. (2005) <doi:10.1093/bioinformatics/bth499>.

This package provides S7-based infrastructure for fitting mediation models, extracting path coefficients, and performing bootstrap inference. Designed as a foundation package for the mediation analysis ecosystem, supporting probmed', RMediation', and medrobust packages. Implements unified interfaces for model fitting across different engines (currently generalized linear models, with future support for mixed models and Bayesian methods), standardized extraction of mediation paths from various model types, and robust bootstrap inference methods. Mediation methods are described in MacKinnon, Lockwood and Williams (2004) <doi:10.1207/s15327906mbr3901_4>, Preacher and Hayes (2008) <doi:10.3758/brm.40.3.879>, Tofighi and MacKinnon (2011) <doi:10.3758/s13428-011-0076-x>, and VanderWeele (2014) <doi:10.1097/EDE.0000000000000121>.

Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as concentration addition', independent action', and generalized concentration addition'.

Inference of Multiscale graphical models with neighborhood selection approach. The method is based on solving a convex optimization problem combining a Lasso and fused-group Lasso penalties. This allows to infer simultaneously a conditional independence graph and a clustering partition. The optimization is based on the Continuation with Nesterov smoothing in a Shrinkage-Thresholding Algorithm solver (Hadj-Selem et al. 2018) <doi:10.1109/TMI.2018.2829802> implemented in python.

Fits mapping-based additive Gaussian process models for experiments in which each component has both a quantitative level and a position in an ordered sequence. Two model structures are available: a compact two-dimensional mapping and a full mapping with one fewer dimension than the number of components. Both models support parameter estimation, point prediction, and plug-in predictive uncertainty. Input checks validate the sequence data and apply consistent scaling to the quantitative inputs. Computationally intensive covariance and gradient calculations are implemented in C++ with Rcpp'. Initial-design functions combine a space-filling Latin hypercube with sequence permutations. The sequence portion can be generated randomly or optimized with simulated annealing or space-filling threshold accepting. Expected improvement can be optimized over both parts of the input, and a sequential interface supports Bayesian optimization of an expensive user-supplied objective. An integrated workflow can generate the initial design, evaluate the objective, and continue the sequential search in one call. The model was introduced by Xiao et al. (2024) <doi:10.1080/01621459.2022.2123335>.

Mine metrics on common places on the web through the power of their APIs (application programming interfaces). It also helps make the data in a format that is easily used for a dashboard or other purposes. There is an associated dashboard template and tutorials that are underdevelopment that help you fully utilize metricminer'.

This package provides functions to calculate the minimum and maximum possible values of Cronbach's alpha when item-level missing data are present. Cronbach's alpha (Cronbach, 1951 <doi:10.1007/BF02310555>) is one of the most widely used measures of internal consistency in the social, behavioral, and medical sciences (Bland & Altman, 1997 <doi:10.1136/bmj.314.7080.572>; Tavakol & Dennick, 2011 <doi:10.5116/ijme.4dfb.8dfd>). However, conventional implementations assume complete data, and listwise deletion is often applied when missingness occurs, which can lead to biased or overly optimistic reliability estimates (Enders, 2003 <doi:10.1037/1082-989X.8.3.322>). This package implements computational strategies including enumeration, Monte Carlo sampling, and optimization algorithms (e.g., Genetic Algorithm, Differential Evolution, Sequential Least Squares Programming) to obtain sharp lower and upper bounds of Cronbach's alpha under arbitrary missing data patterns. The approach is motivated by Manski's partial identification framework and pessimistic bounding ideas from optimization literature.

Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.

This package provides functions to read in and manipulate air quality model output from Models3-formatted files. This format is used by the Community Multiscale Air Quality (CMAQ) model.

Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.

It contains six common multi-category classification accuracy evaluation measures. All of these measures could be found in Li and Ming (2019) <doi:10.1002/sim.8103>. Specifically, Hypervolume Under Manifold (HUM), described in Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>. Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>. Polytomous Discrimination Index (PDI), described in Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>. Li et al. (2018) <doi:10.1177/0962280217692830>. PDI with variance estimation using Dover et al. (2021) <doi:10.1002/sim.9187>. We described all these above measures and our mcca package in Li, Gao and D'Agostino (2019) <doi:10.1002/sim.8103>.

Colour palettes and helper functions for visualising Mycobacterium tuberculosis genomic and epidemiological data with ggplot2 and ggtree'. The package provides predefined palettes, scale functions, tree/cladogram helpers, and convenient preview tools to ensure consistent branding in pathogen-omics visualisations. The palettes were developed as part of the mycolorsTB project <https://github.com/PathoGenOmics-Lab/mycolorsTB>.

Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005) <DOI:10.1111/j.1467-9868.2005.00499.x>.

Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports APSystems', Enphase', and Fronius microinverters.

An R interface for the Java Machine Learning for Language Toolkit (mallet) <http://mallet.cs.umass.edu/> to estimate probabilistic topic models, such as Latent Dirichlet Allocation. We can use the R package to read textual data into mallet from R objects, run the Java implementation of mallet directly in R, and extract results as R objects. The Mallet toolkit has many functions, this wrapper focuses on the topic modeling sub-package written by David Mimno. The package uses the rJava package to connect to a JVM.

This package contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals and tests, and generation of similarity matrices.

It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Global testing for regression discontinuity designs with more than one running variable. The function cef_disc_test() is used for testing whether there exist non-zero treatment effects along the boundary of the treated region. The function density_disc_test() is used for testing whether there exist discontinuities in the joint density of the running variables along the boundary of the treated region. The methodology follows Samiahulin (2026), "Global Testing for Regression Discontinuity Designs with Multiple Running Variables" <doi:10.48550/arXiv.2602.03819>.