Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

This package provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

For a data matrix with m rows and n columns (m>=n), the power method is used to compute, simultaneously, the eigendecomposition of a square symmetric matrix. This result is used to obtain the singular value decomposition (SVD) and the principal component analysis (PCA) results. Compared to the classical SVD method, the first r singular values can be computed.

This package provides a function kitten() which creates cute little packages which pass R package checks. This sets it apart from package.skeleton() which it calls, and which leaves imperfect files behind. As this is not exactly helpful for beginners, kitten() offers an alternative. Unit test support can be added via the tinytest package (if present), and documentation-creation support can be added via roxygen2 (if present).

This package provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Numerical integration of continuously differentiable functions f(x,y) over simple closed polygonal domains. The following cubature methods are implemented: product Gauss cubature (Sommariva and Vianello, 2007, <doi:10.1007/s10543-007-0131-2>), the simple two-dimensional midpoint rule (wrapping spatstat.geom functions), and adaptive cubature for radially symmetric functions via line integrate() along the polygon boundary (Meyer and Held, 2014, <doi:10.1214/14-AOAS743>, Supplement B). For simple integration along the axes, the cubature package is more appropriate.

This package provides a suite of empirical Bayes methods to use in pharmacovigilance. Contains various model fitting and post-processing functions. For more details see Tan et al. (2025) <doi:10.1002/sim.70195>, <doi:10.48550/arXiv.2512.01057>; Koenker and Mizera (2014) <doi:10.1080/01621459.2013.869224>; Efron (2016) <doi:10.1093/biomet/asv068>.

This package provides publicationâ quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2â D latent maps, and itemâ similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>.

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.

Constructs a model-averaged propensity score as a convex combination of candidate propensity score models, with mixing weights selected on a simplex grid to optimize covariate or prognostic-score balance, implementing the method of Kabata, Stuart and Shintani (2024) <doi:10.1186/s12874-024-02350-y>. Prognostic scores follow Hansen (2008) <doi:10.1093/biomet/asn004>: outcome models are fit on untreated units only. The resulting score is designed to be supplied directly to the matchit() function of MatchIt as a distance measure or to the weightit() function of WeightIt as a propensity score, with balance assessment via cobalt'.

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. See Signorelli (2024) <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021) <doi:10.1002/sim.9178> for details.

Analyzis and filtering of phylogenomics datasets. It takes an input either a collection of gene trees (then transformed to matrices) or directly a collection of gene matrices and performs an iterative process to identify what species in what genes are outliers, and whose elimination significantly improves the concordance between the input matrices. The methods builds upon the Distatis approach (Abdi et al. (2005) <doi:10.1101/2021.09.08.459421>), a generalization of classical multidimensional scaling to multiple distance matrices.

This package implements a unified framework of parametric simplex method for a variety of sparse learning problems (e.g., Dantzig selector (for linear regression), sparse quantile regression, sparse support vector machines, and compressive sensing) combined with efficient hyper-parameter selection strategies. The core algorithm is implemented in C++ with Eigen3 support for portable high performance linear algebra. For more details about parametric simplex method, see Haotian Pang (2017) <https://papers.nips.cc/paper/6623-parametric-simplex-method-for-sparse-learning.pdf>.

This package provides a trait-based workflow for evaluating whether phylogenetic relatedness is informative about similarity in measured quantitative traits within focal species pools and across multiple communities. Functions support trait data integration, taxon-specific trait extraction, coverage assessment, optional principal component analysis, and estimation of phylogenetic signal using Pagel's lambda or Blomberg's K. Curated quantitative trait datasets are included for plants, birds, mammals, reptiles, amphibians, and fishes. Paired simulations assess how observed patterns of missing trait data affect Pagel's lambda estimates and significance classifications for individual traits. Methods for quantifying phylogenetic signal are based on Pagel (1999) <doi:10.1038/44766>, Blomberg et al. (2003) <doi:10.1111/j.0014-3820.2003.tb00285.x>, and Münkemüller et al. (2012) <doi:10.1111/j.2041-210X.2012.00196.x>.

This package provides tools for exploratory process data analysis. Process data refers to the data describing participants problem-solving processes in computer-based assessments. It is often recorded in computer log files. This package provides functions to read, process, and write process data. It also implements two feature extraction methods to compress the information stored in process data into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are wrappers of functions in keras'.

This package provides a comprehensive framework for planning and executing analyses in R. It provides a structured approach to running the same function multiple times with different arguments, executing multiple functions on the same datasets, and creating systematic analyses across multiple strata or variables. The framework is particularly useful for applying the same analysis across multiple strata (e.g., locations, age groups), running statistical methods on multiple variables (e.g., exposures, outcomes), generating multiple tables or graphs for reports, and creating systematic surveillance analyses. Key features include efficient data management, structured analysis planning, flexible execution options, built-in debugging tools, and hash-based caching.

This package implements tools for the analysis of partially ordered data, with a particular focus on the evaluation of multidimensional systems of indicators and on the analysis of poverty. References, Fattore M. (2016) <doi:10.1007/s11205-015-1059-6> Fattore M., Arcagni A. (2016) <doi:10.1007/s11205-016-1501-4> Arcagni A. (2017) <doi:10.1007/978-3-319-45421-4_19>.

Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

This package provides a Shiny dashboard for exploring Reporters Without Borders (RWB) Press Freedom Index data from 2002 to the present. Combines RWB scores with United Nations M49 geographic classifications to enable comparisons across countries, regions, and time.

Design, backtest, and analyze portfolio strategies using simple, English-like function chains. Includes technical indicators, flexible stock selection, portfolio construction methods (equal weighting, signal weighting, inverse volatility, hierarchical risk parity), and a compact backtesting engine for portfolio returns, drawdowns, and summary metrics.

Stochastic block model used for dynamic graphs represented by Poisson processes. To model recurrent interaction events in continuous time, an extension of the stochastic block model is proposed where every individual belongs to a latent group and interactions between two individuals follow a conditional inhomogeneous Poisson process with intensity driven by the individualsâ latent groups. The model is shown to be identifiable and its estimation is based on a semiparametric variational expectation-maximization algorithm. Two versions of the method are developed, using either a nonparametric histogram approach (with an adaptive choice of the partition size) or kernel intensity estimators. The number of latent groups can be selected by an integrated classification likelihood criterion. Y. Baraud and L. Birgé (2009). <doi:10.1007/s00440-007-0126-6>. C. Biernacki, G. Celeux and G. Govaert (2000). <doi:10.1109/34.865189>. M. Corneli, P. Latouche and F. Rossi (2016). <doi:10.1016/j.neucom.2016.02.031>. J.-J. Daudin, F. Picard and S. Robin (2008). <doi:10.1007/s11222-007-9046-7>. A. P. Dempster, N. M. Laird and D. B. Rubin (1977). <http://www.jstor.org/stable/2984875>. G. Grégoire (1993). <http://www.jstor.org/stable/4616289>. L. Hubert and P. Arabie (1985). <doi:10.1007/BF01908075>. M. Jordan, Z. Ghahramani, T. Jaakkola and L. Saul (1999). <doi:10.1023/A:1007665907178>. C. Matias, T. Rebafka and F. Villers (2018). <doi:10.1093/biomet/asy016>. C. Matias and S. Robin (2014). <doi:10.1051/proc/201447004>. H. Ramlau-Hansen (1983). <doi:10.1214/aos/1176346152>. P. Reynaud-Bouret (2006). <doi:10.3150/bj/1155735930>.

Perform inference in the secondary analysis setting with linked data potentially containing mismatch errors. Only the linked data file may be accessible and information about the record linkage process may be limited or unavailable. Implements the General Framework for Regression with Mismatched Data developed by Slawski et al. (2023) <doi:10.48550/arXiv.2306.00909>. The framework uses a mixture model for pairs of linked records whose two components reflect distributions conditional on match status, i.e., correct match or mismatch. Inference is based on composite likelihood and the Expectation-Maximization (EM) algorithm. The package currently supports Cox Proportional Hazards Regression (right-censored data only) and Generalized Linear Regression Models (Gaussian, Gamma, Poisson, and Logistic (binary models only)). Information about the underlying record linkage process can be incorporated into the method if available (e.g., assumed overall mismatch rate, safe matches, predictors of match status, or predicted probabilities of correct matches).

Conduct post-selection inference for regression coefficients in linear models after they have been selected by adjusted R squared. The p-values and confidence intervals are valid after model selection with the same data. This allows the user to use all data for both model selection and inference without losing control over the type I error rate. The provided tests are more powerful than data splitting, which bases inference on less data since it discards all information used for selection.