This package provides a novel sufficient-dimension reduction method is robust against outliers using alpha-distance covariance and manifold-learning in dimensionality reduction problems. Please refer Hsin-Hsiung Huang, Feng Yu & Teng Zhang (2024) <doi:10.1080/10485252.2024.2313137> for the details.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.

Allows interaction with Interactive Brokers Trader Workstation <https://interactivebrokers.github.io/tws-api/>. Handles the connection over the network and the exchange of messages. Data is encoded and decoded between user and wire formats. Data structures and functionality closely mirror the official implementations.

This package performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are Method A / Method B and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well.

This package provides tools to (i) check consistency of a finite set of consumer demand observations with a number of revealed preference axioms at a given efficiency level, (ii) compute goodness-of-fit indices when the data do not obey the axioms, and (iii) compute power against uniformly random behavior.

Computation of one-, two- and three-dimensional pseudo-observations based on recurrent events and terminal events. Generalised linear models are fitted using generalised estimating equations. Technical details on the bivariate procedure can be found in "Bivariate pseudo-observations for recurrent event analysis with terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>.

This package provides a memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. rMVP contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).

Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

An easy-to-use tool to employ interactivity in every-day exploratory analysis. It contains a collection of most commonly used types of charts (such as scatter plots, line plots, heatmaps, bar charts), which can be linked to each other or to other interactive elements with just few lines of code.

Provide function for work with AcademyOcean API <https://academyocean.com/api>.

This package provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies.

This package provides functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.

Fit statistical models based on the Dawid-Skene model - Dawid and Skene (1979) <doi:10.2307/2346806> - to repeated categorical rating data. Full Bayesian inference for these models is supported through the Stan modelling language. rater also allows the user to extract and plot key parameters of these models.

Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Utility functions to download data from the RESOURCECODE hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.

This package provides a collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.

Modified Poisson and least-squares regression analyses for binary outcomes of Zou (2004) <doi:10.1093/aje/kwh090> and Cheung (2007) <doi:10.1093/aje/kwm223> have been standard multivariate analysis methods to estimate risk ratio and risk difference in clinical and epidemiological studies. This R package involves an easy-to-handle function to implement these analyses by simple commands. Missing data analysis tools (multiple imputation) are also involved. In addition, recent studies have shown the ordinary robust variance estimator possibly has serious bias under small or moderate sample size situations for these methods. This package also provides computational tools to calculate alternative accurate confidence intervals (Noma and Gosho (2024) <Forthcoming>).

This package provides XML parsing capability through the Rapidxml C++ header-only library.

The R Analytic Tool To Learn Easily (Rattle) provides a collection of utilities functions for the data scientist. A Gnome (RGtk2) based graphical interface is included with the aim to provide a simple and intuitive introduction to R for data science, allowing a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, build and evaluate models, and export models as PMML (predictive modelling markup language) or as scores. A key aspect of the GUI is that all R commands are logged and commented through the log tab. This can be saved as a standalone R script file and as an aid for the user to learn R or to copy-and-paste directly into R itself. Note that RGtk2 and cairoDevice have been archived on CRAN. See <https://rattle.togaware.com> for installation instructions.

This package provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine <https://openrefine.org/>. More info on key collision and ngram fingerprint can be found here <https://openrefine.org/docs/technical-reference/clustering-in-depth>.

This package implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects.

Generate random user data from the Random User Generator API. For more information, see <https://randomuser.me/>.