The GeneCycle package implements the approaches of Wichert et al. (2004) <doi:10.1093/bioinformatics/btg364>, Ahdesmaki et al. (2005) <doi:10.1186/1471-2105-6-117> and Ahdesmaki et al. (2007) <DOI:10.1186/1471-2105-8-233> for detecting periodically expressed genes from gene expression time series data.

Access to The Guardian newspaper's open API <https://open-platform.theguardian.com/>, containing all articles published in The Guardian from 1999 to the present, including article text, metadata, tags and contributor information. An API key and registration is required.

This package provides a set of geometries to make line plots a little bit nicer. Use along with ggplot2 to: - Improve the clarity of line plots with many overlapping lines - Draw more realistic worms.

Computes marginal likelihood in Gaussian graphical models through a novel telescoping block decomposition of the precision matrix which allows estimation of model evidence. The top level function used to estimate marginal likelihood is called evidence(), which expects the prior name, data, and relevant prior specific parameters. This package also provides an MCMC prior sampler using the same underlying approach, implemented in prior_sampling(), which expects a prior name and prior specific parameters. Both functions also expect the number of burn-in iterations and the number of sampling iterations for the underlying MCMC sampler.

This package provides tools for exploratory geospatial distribution dynamics with sf objects and tidy data. Provides pooled and time-specific classification of longitudinal spatial values, classic discrete Markov transition matrices, spatial Markov matrices conditioned on spatial-lag classes, endpoint and adjacent rank mobility summaries, and ggplot2 visualizations. Methods follow Rey (2001) <doi:10.1111/j.1538-4632.2001.tb00444.x> and Rey et al. (2016) <doi:10.1007/s10109-016-0234-x>; design is inspired by the Python PySAL giddy package <https://pysal.org/giddy/>.

This package provides tools for working with polygons with holes in ggplot2', with a new geom for drawing a polypath applying the evenodd or winding rules.

This package provides a statistical hypothesis test for conditional independence. Given residuals from a sufficiently powerful regression, it tests whether the covariance of the residuals is vanishing. It can be applied to both discretely-observed functional data and multivariate data. Details of the method can be found in Anton Rask Lundborg, Rajen D. Shah and Jonas Peters (2022) <doi:10.1111/rssb.12544>.

This package provides browser-native WebGL rendering for R graphics through htmlwidgets'. The package supports grammar-style graphics workflows and renderer-ready specifications for dense analytical and scientific scenes, including point, line, trajectory, raster, vector, mesh, and surface layers, shader-driven display modes, timeline controls, structured views, selection metadata, and publication-oriented static export helpers. Rendering stays in the browser, and the core package remains cross-platform without requiring CUDA', Metal', or OpenCL toolchains.

Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with boot provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi10.1080/03610918.2015.1122048.

Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.

Gaussian mixture models and k-means for topic analysis of dense document vectors. The underlying clustering functions rely on the Armadillo library.

Selected utilities, in particular geoms and stats functions, extending the ggplot2 package. This package imports functions from EnvStats <doi:10.1007/978-1-4614-8456-1> by Millard (2013), ggpp <https://CRAN.R-project.org/package=ggpp> by Aphalo et al. (2023) and ggstats <doi:10.5281/zenodo.10183964> by Larmarange (2023), and then exports them. This package also contains modified code from ggquickeda <https://CRAN.R-project.org/package=ggquickeda> by Mouksassi et al. (2023) for Kaplan-Meier lines and ticks additions to plots. All functions are tested to make sure that they work reliably.

Provide specialized ggplot2 layers and scales for spatial uncertainty visualization, including bivariate choropleth maps, pixel maps, glyph maps, and exceedance probability maps.

Declares, retrieves, verifies, tracks and actively manages external data dependencies too large or too fast-moving to ship inside a package. Resources are identified by package, name and version, pinned to a Secure Hash Algorithm (SHA-256) checksum, and resolved through an explicit policy so that the same installed package always resolves the same bytes. A registry served from a remote host may be signed with Ed25519 and verified against a key the declaring package ships, so the declaration and the key that vouches for it arrive by different routes. Hashing follows National Institute of Standards and Technology (2015) "Secure Hash Standard" <doi:10.6028/NIST.FIPS.180-4>; signing follows Bernstein, Duif, Lange, Schwabe and Yang (2012) "High-Speed High-Security Signatures" <doi:10.1007/s13389-012-0027-1> and Josefsson and Liusvaara (2017) "Edwards-Curve Digital Signature Algorithm (EdDSA)" <doi:10.17487/RFC8032>. Designed for reproducible offline use and graceful behaviour during package checks.

Circular genomic permutation approach uses genome wide association studies (GWAS) results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All single nucleotide polymorphisms (SNPs) in the GWAS are placed in a circular genome according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses Fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values and SNPs annotation to genes. Pathways can be obtained from within the package or can be provided by the user. Cabrera et al (2012) <doi:10.1534/g3.112.002618> .

After fitting a Generalized Additive (Mixed) Model, the next step is often to obtain predicted values for certain combinations of predictors for visualization of estimated effects in the model. It involves constructing a new data frame, add predicted values, and finally makes a (contour) plot. This package is intended to facilitate these steps to visualize estimated effects in a generalized additive model. The underlying modeling methodology is described in Wood (2017, ISBN:9781498728331).

Identifies implausible anthropometric (e.g., height, weight) measurements in irregularly spaced longitudinal datasets, such as those from electronic health records.

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

Core C++ engine for glmbayes': envelope-based iid linear and generalized linear model samplers, prior-family routing, and optional OpenCL acceleration. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. Intended as a developer backend for the glmbayes formula interface; end users should use glmbayes for modelling with interfaces analogous to lm and glm'. Mixed-model engines are planned for a future release.

The geom_rain() function adds different geoms together using ggplot2 to create raincloud plots.

This package implements DB-TARF (Design-Based Targeted Adaptive Residual Forest) for large-scale digital soil and ecological mapping evaluated under the design-based paradigm of Wadoux et al. (2021) <doi:10.1016/j.ecolmodel.2021.109692>. A random forest is augmented by a cross-fitted, out-of-fold-selected residual correction (residual forests, ordinary kriging, recalibration), together with design-based conformal prediction intervals.

Includes a collection of geographical analysis functions aimed primarily at ecology and conservation science studies, allowing processing of both point and raster data. Now integrates SPECTRE (<https://biodiversityresearch.org/spectre/>), a dataset of global geospatial threat data, developed by the authors.

This package provides Bayesian linear and generalized linear model fitting with independent and identically distributed (iid) posterior samples. The main functions mirror R's lm() and glm() interfaces while adding prior family specifications for Gaussian, Poisson, binomial, and Gamma models with log-concave likelihoods. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. The package also includes tools for prior setup, posterior summaries, prediction, diagnostics, simulation, vignettes, and optional OpenCL acceleration for larger models.

These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.