We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.

This package provides a suite of methods to fit and predict case count data using a compartmental SIRS (Susceptible â Infectious â Recovered â Susceptible) model, based on an assumed specification of the effective reproduction number. The significance of this approach is that it relates epidemic progression to the average number of contacts of infected individuals, which decays as a function of the total susceptible fraction remaining in the population. The main functions are pred.curve(), which computes the epidemic curve for a set of parameters, and estimate.mle(), which finds the best fitting curve to observed data. The easiest way to pass arguments to the functions is via a config file, which contains input settings required for prediction, and the package offers two methods, navigate_to_config() which points the user to the configuration file, and re_predict() for starting the fit-predict process. The main model was published in Razvan G. Romanescu et al. <doi:10.1016/j.epidem.2023.100708>.

This package provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.

Truncated Newton function minimization with bounds constraints based on the Matlab'/'Octave codes of Stephen Nash.

Extends R Commander with a unified menu of new and pre-existing statistical functions related to public management and policy analysis statistics. Functions and menus have been renamed according to the usage in PMGT 630 in the Master of Public Administration program at Brigham Young University.

This package provides a programmatic interface to the Request Tracker (RT) HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. RT is a popular ticket tracking system.

Bindings for additional models for use with the parsnip package. Models include prediction rule ensembles (Friedman and Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>.

Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation. A publication describing the method has the following citation: Gregory J. Hunt, Mark A. Dane, James E. Korkola, Laura M. Heiser & Johann A. Gagnon-Bartsch (2020) "Automatic Transformation and Integration to Improve Visualization and Discovery of Latent Effects in Imaging Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2020.1741379>.

Represents high-dimensional data as tables of features, samples and measurements, and a design list for tracking the meaning of individual variables. Using this format, filtering, normalization, and other transformations of a dataset can be carried out in a flexible manner. romic takes advantage of these transformations to create interactive shiny apps for exploratory data analysis such as an interactive heatmap.

This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.".

Using the efficient implementation in the Boost C++ library, functions are provided to generate vectors of Universally Unique Identifiers (UUID) from R supporting random (version 4), name (version 5) and time (version 7) UUIDs'. The initial repository was at <https://gitlab.com/artemklevtsov/rcppuuid>.

Systematic conservation prioritization with robust optimization techniques. This is important because conservation prioritizations typically only consider the most likely outcome associated with a conservation action (e.g., establishing a protected area will safeguard a threatened species population) and fail to consider other outcomes and their consequences for meeting conservation objectives. By extending the prioritizr package, this package can be used to generate conservation prioritizations that account of uncertainty in the climate change scenario projections, species distribution models, ecosystem service models, and measurement errors. In particular, prioritizations can be generated to be fully robust to uncertainty by minimizing (or maximizing) objectives under the worst possible outcome. Since reducing the associated with achieving conservation objectives may sacrifice other objectives (e.g., minimizing protected area implementation costs), prioritizations can also be generated to be partially robust based on a specified confidence level parameter. Partially robust prioritizations can be generated based on the chance constrained programming problem (Charnes & Cooper 1959, <doi:10.1287/mnsc.6.1.73>) and the conditional value-at-risk problem (Rockafellar & Uryasev 2000, <doi:10.21314/JOR.2000.038>).

Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), ricu provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.

Load data from Yandex Direct API V5 <https://yandex.ru/dev/direct/doc/dg/concepts/about-docpage> into R. Provide function for load lists of campaings, ads, keywords and other objects from Yandex Direct account. Also you can load statistic from API Reports Service <https://yandex.ru/dev/direct/doc/reports/reports-docpage>. And allows keyword bids management.

This package implements regression calibration methods for correcting measurement error in regression models using external or internal reliability studies. Methods are described in Carroll, Ruppert, Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear Models: A Modern Perspective" <doi:10.1201/9781420010138>.

Datasets with energy consumption data of different data measurement frequencies. The data stems from several publicly funded research projects of the Chair of Information Systems and Energy Efficient Systems at the University of Bamberg.

Seamless extraction of river networks from digital elevation models data. The package allows analysis of digital elevation models that can be either externally provided or downloaded from open source repositories (thus interfacing with the elevatr package). Extraction is performed via the D8 flow direction algorithm of TauDEM (Terrain Analysis Using Digital Elevation Models), thus interfacing with the traudem package. Resulting river networks are compatible with functions from the OCNet package. See Carraro (2023) <doi:10.5194/hess-27-3733-2023> for a presentation of the package.

Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It offers full access to options and outputs of TRAMO-SEATS (Time series Regression with ARIMA noise, Missing values and Outliers - Signal Extraction in ARIMA Time Series), including TRAMO modelling (ARIMA model with outlier detection and trading days adjustment). ARIMA = AutoRegressive Integrated Moving Average.

This package provides functions for reading data sets in different formats for testing machine learning tools are provided. This allows to run a loop over several data sets in their original form, for example if they are downloaded from UCI Machine Learning Repository. The data are not part of the package and have to be downloaded separately.

This package creates interactive analytic graphs with R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as D3.js graphs embedded in a web page ready for its interactive analysis and exploration.

Fit Class Cover Catch Digraph Classification models that can be used in machine learning. Pure and proper and random walk approaches are available. Methods are explained in Priebe et al. (2001) <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) <doi:10.48550/arXiv.1904.04564>.

This package creates the radar-boxplot, a plot that was created by the author during his Ph.D. in forest resources. The radar-boxplot is a visualization feature suited for multivariate classification/clustering. It provides an intuitive deep understanding of the data.

The Google FarmHash family of hash functions is used by the Google BigQuery data warehouse via the FARM_FINGERPRINT function. This package permits to calculate these hash digest fingerprints directly from R, and uses the included FarmHash files written by G. Pike and copyrighted by Google, Inc.

This package provides a tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.