This package contains all phrasal verbs listed in <https://www.englishclub.com/ref/Phrasal_Verbs/> as data frame. Useful for educational purpose as well as for text mining.

This package provides functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

This package implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.

This package provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in EpiEstim or in earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class projections which extends matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.

This package provides functions for the construction of Petri Nets. Petri Nets can be replayed by firing enabled transitions. Silent transitions will be hidden by the execution handler. Also includes functionalities for the visualization of Petri Nets and export of Petri Nets to PNML (Petri Net Markup Language) files.

Full dynamic system to describe and forecast the spread and the severity of a developing pandemic, based on available data. These data are number of infections, hospitalizations, deaths and recoveries notified each day. The system consists of three transitions, infection-infection, infection-hospital and hospital-death/recovery. The intensities of these transitions are dynamic and estimated using non-parametric local linear estimators. The package can be used to provide forecasts and survival indicators such as the median time spent in hospital and the probability that a patient who has been in hospital for a number of days can leave it alive. Methods are described in Gámiz, Mammen, Martà nez-Miranda, and Nielsen (2024) <doi:10.48550/arXiv.2308.09918> and <doi:10.48550/arXiv.2308.09919>.

This package provides an R implementation of the Particle Metropolis within Gibbs sampler for model parameter, covariance matrix and random effect estimation. A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing the package is available at <https://university-of-newcastle-research.github.io/samplerDoc/> and includes several detailed examples, some background and troubleshooting steps.

Builds and runs c++ code for classes that encapsulate state space model, particle filtering algorithm pairs. Algorithms include the Bootstrap Filter from Gordon et al. (1993) <doi:10.1049/ip-f-2.1993.0015>, the generic SISR filter, the Auxiliary Particle Filter from Pitt et al (1999) <doi:10.2307/2670179>, and a variety of Rao-Blackwellized particle filters inspired by Andrieu et al. (2002) <doi:10.1111/1467-9868.00363>. For more details on the c++ library pf', see Brown (2020) <doi:10.21105/joss.02599>.

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.

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons. Includes palettes for colour-blind viewers.

Particle swarm optimization - a basic variant.

This package provides functions for obtaining the density, random deviates and maximum likelihood estimates of the Poisson lognormal distribution and the bivariate Poisson lognormal distribution.

This package provides access to a high performant random distribution sampler for the Polya Gamma Distribution using either C++ headers for Rcpp or RcppArmadillo and R'.

XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.

This package provides tools for performing disproportionality analysis using the information component, proportional reporting rate and the reporting odds ratio. The anticipated use is passing data to the da() function, which executes the disproportionality analysis. See Norén et al (2011) <doi:10.1177/0962280211403604> and Montastruc et al (2011) <doi:10.1111/j.1365-2125.2011.04037.x> for further details.

Market odds from from Pinnacle, an online sports betting bookmaker (see <https://www.pinnacle.com> for more information). Included are datasets for the Major League Baseball (MLB) 2016 season and the USA election 2016. These datasets can be used to build models and compare statistical information with the information from prediction markets.The Major League Baseball (MLB) 2016 dataset can be used for sabermetrics analysis and also can be used in conjunction with other popular Major League Baseball (MLB) datasets such as Retrosheets or the Lahman package by merging by GameID.

Cluster analysis via nonparametric density estimation is performed. Operationally, the kernel method is used throughout to estimate the density. Diagnostics methods for evaluating the quality of the clustering are available. The package includes also a routine to estimate the probability density function obtained by the kernel method, given a set of data with arbitrary dimensions.

This package provides a collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.

Enhanced RTF wrapper written in R for use with existing R tables packages such as Huxtable or GT'. This package fills a gap where tables in certain packages can be written out to RTF, but cannot add certain metadata or features to the document that are required/expected in a report for a regulatory submission, such as multiple levels of titles and footnotes, making the document landscape, and controlling properties such as margins.

Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

This package provides an R interface to the PCATS API <https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit tasks and retrieve results.

Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).

This package provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.