This package provides an implementation of bee swarm plots. The bee swarm plot is a one-dimensional scatter plot like stripchart, but with closely-packed, non-overlapping points.

This package performs Bayesian calibration of computer models as per Kennedy and O'Hagan 2001. The package includes routines to find the hyperparameters and parameters; see the help page for stage1() for a worked example using the toy dataset. A tutorial is provided in the calex.Rnw vignette; and a suite of especially simple one dimensional examples appears in inst/doc/one.dim/.

Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi10.1007/s11222-005-4070-y>) and the Fourier inversion (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.

This package provides functions relating to time series analysis and computational finance.

This package provides routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). It includes routines that:

1. generate gradient and jacobian matrices (full and banded),

2. find roots of non-linear equations by the Newton-Raphson method,

3. estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the Newton-Raphson method, or by dynamically running,

4. solve the steady-state conditions for uni- and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach).

This package provides an extension to the Shiny web application framework for R, making it easy to create attractive dashboards.

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

This package provides a set of tools for displaying, modeling and analysing multivariate abundance data in community ecology.

The aim of SHAPforxgboost is to aid in visual data investigations using SHAP (Shapley additive explanation) visualization plots for XGBoost. It provides summary plot, dependence plot, interaction plot, and force plot. It relies on the XGBoost package to produce SHAP values.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

This package provides a collection of functions to compute the standardized effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A). The computation algorithms have been optimized to allow efficient computation even with very large data sets.

This is a package supporting cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; doi:10.1007/s11336-009-9125-0). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015)

This package provides data sets from project Mosaic http://mosaic-web.org used to teach mathematics, statistics, computation and modeling.

This package provides ggplot2 geoms filled with various patterns. It includes a patterned version of every ggplot2 geom that has a region that can be filled with a pattern. It provides a suite of ggplot2 aesthetics and scales for controlling pattern appearances. It supports over a dozen builtin patterns (every pattern implemented by gridpattern) as well as allowing custom user-defined patterns.

This package provides functions useful in the design and ANOVA of experiments. The content falls into the following groupings:

1. data,

2. factor manipulation functions,

3. design functions,

4. ANOVA functions,

5. matrix functions,

6. projector and canonical efficiency functions, and

7. miscellaneous functions.

There is a vignette called DesignNotes describing how to use the design functions for randomizing and assessing designs. The ANOVA functions facilitate the extraction of information when the Error function has been used in the call to aov.

This package is a feature selection package of the mlr3 ecosystem. It selects the optimal feature set for any mlr3 learner. The package works with several optimization algorithms e.g. random search, Recursive feature elimination, and genetic search. Moreover, it can automatically optimize learners and estimate the performance of optimized feature sets with nested resampling.

This package lets you build regression models using the techniques in Friedman's papers "Fast MARS" and "Multivariate Adaptive Regression Splines" <doi:10.1214/aos/1176347963>. The term "MARS" is trademarked and thus not used in the name of the package.

This package provides routines for the polynomial spline fitting routines hazard regression, hazard estimation with flexible tails, logspline, lspec, polyclass, and polymars.

This package provides a system for reporting messages, which offers certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".

This package is an R package designed for QC, analysis, and exploration of single cell RNA-seq data. It easily enables widely-used analytical techniques, including the identification of highly variable genes, dimensionality reduction; PCA, ICA, t-SNE, standard unsupervised clustering algorithms; density clustering, hierarchical clustering, k-means, and the discovery of differentially expressed genes and markers.

Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem.

This package provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.

This package defines the generic method extract and provides openMP support as needed in several packages like aws, adimpro, fmri, and dwi.