Perform sparse estimation of a Gaussian graphical model (GGM) with node aggregation through variable clustering. Currently, the package implements the clusterpath estimator of the Gaussian graphical model (CGGM) (Touw, Alfons, Groenen & Wilms, 2025; <doi:10.48550/arXiv.2407.00644>).

This package provides functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.

Coalescent simulators can rapidly simulate biological sequences evolving according to a given model of evolution. You can use this package to specify such models, to conduct the simulations and to calculate additional statistics from the results (Staab, Metzler, 2016 <doi:10.1093/bioinformatics/btw098>). It relies on existing simulators for doing the simulation, and currently supports the programs ms', msms and scrm'. It also supports finite-sites mutation models by combining the simulators with the program seq-gen'. Coala provides functions for calculating certain summary statistics, which can also be applied to actual biological data. One possibility to import data is through the PopGenome package (<https://github.com/pievos101/PopGenome>).

This package implements cointegration/co-trending rank selection algorithm in Guo and Shintani (2013) "Consistent co-trending rank selection when both stochastic and nonlinear deterministic trends are present". The Econometrics Journal 16: 473-483 <doi:10.1111/j.1368-423X.2012.00392.x>. Numbered examples correspond to Feb 2011 preprint <http://www.fas.nus.edu.sg/ecs/events/seminar/seminar-papers/05Apr11.pdf>.

This package provides access to consolidated information from the Brazilian Federal Government Payment Card. Includes functions to retrieve, clean, and organize data directly from the Transparency Portal <https://portaldatransparencia.gov.br/download-de-dados/cpgf/> and a curated dataset hosted on the Open Science Framework <https://osf.io/z2mxc/>. Useful for public spending analysis, transparency research, and reproducible workflows in auditing or investigative journalism.

The Citation File Format version 1.2.0 <doi:10.5281/zenodo.5171937> is a human and machine readable file format which provides citation metadata for software. This package provides core utilities to generate and validate this metadata.

This package implements a joint cointegration testing approach that combines Engle-Granger, Johansen maximum eigenvalue, Boswijk, and Banerjee tests into a unified test-statistic for the null of non-cointegration. Also see Bayer and Hanck (2013) <doi:10.1111/j.1467-9892.2012.00814.x>.

Code for a variety of nonlinear conditional independence tests: Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>), Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>), Invariant environment prediction, Invariant target prediction, Invariant residual distribution test, Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).

Convolute probabilistic distributions using the random generator function of each distribution. A new random number generator function is created that perform the mathematical operation on the individual random samples from the random generator function of each distribution. See the documentation for examples.

The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides utilities to generate, parse, and modify codemeta.json files automatically for R packages, as well as tools and examples for working with codemeta.json JSON-LD more generally.

Mainly used to build tables that are commonly presented for bio-medical/health research, such as basic characteristic tables or descriptive statistics.

Computes classification accuracy and consistency indices under Item Response Theory. Implements the total score IRT-based methods in Lee, Hanson & Brennen (2002) and Lee (2010), the IRT-based methods in Rudner (2001, 2005), and the total score nonparametric methods in Lathrop & Cheng (2014). For dichotomous and polytomous tests.

Correcting area under ROC (AUC) for measurement error based on probit-shift model.

Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.

Gather boxscore and play-by-play data from the Canadian Elite Basketball League (CEBL) <https://www.cebl.ca> to create a repository of basic and advanced statistics for teams and players.

This package creates a 3D data cube view of a RasterStack/Brick, typically a collection/array of RasterLayers (along z-axis) with the same geographical extent (x and y dimensions) and resolution, provided by package raster'. Slices through each dimension (x/y/z), freely adjustable in location, are mapped to the visible sides of the cube. The cube can be freely rotated. Zooming and panning can be used to focus on different areas of the cube.

Compute ranking and rating based on competition results. Methods of different nature are implemented: with fixed Head-to-Head structure, with variable Head-to-Head structure and with iterative nature. All algorithms are taken from the book Whoâ s #1?: The science of rating and ranking by Amy N. Langville and Carl D. Meyer (2012, ISBN:978-0-691-15422-0).

Providing a set of functions to easily generate and iterate complex networks. The functions can be used to generate realistic networks with a wide range of different clustering, density, and average path length. For more information consult research articles by Amiyaal Ilany and Erol Akcay (2016) <doi:10.1093/icb/icw068> and Ilany and Erol Akcay (2016) <doi:10.1101/026120>, which have inspired many methods in this package.

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014) <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015) <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>, and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998) <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>. Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree() implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and plot_morphospace_stack() implements Foote (1993) <doi:10.1017/S0094837300015864>. Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>. calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi).

This package provides an array of statistical models common in causal inference such as standardization, IP weighting, propensity matching, outcome regression, and doubly-robust estimators. Estimates of the average treatment effects from each model are given with the standard error and a 95% Wald confidence interval (Hernan, Robins (2020) <https://miguelhernan.org/whatifbook/>).

This package provides functions for clustering regions that form convergence clubs, according to the definition of Phillips and Sul (2009) <doi:10.1002/jae.1080>. A package description is available in Sichera and Pizzuto (2019).

Implementation of transductive conformal prediction (see Vovk, 2013, <doi:10.1007/978-3-642-41142-7_36>) and inductive conformal prediction (see Balasubramanian et al., 2014, ISBN:9780124017153) for classification problems.

This package provides functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way MANOVA design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The candisc package generalizes this to higher-way MANOVA designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an mlm via the plot.candisc and heplot.candisc methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative. Methods for linear discriminant analysis are now included.