This is an unofficial package aimed at automating the import of LISREL output in R.

This package provides an API for https://orcid.org. Functions include searching for people, searching by DOI, or searching by Orcid ID.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

This package provides tools for regression subset selection, including exhaustive search.

Rasterize only specific layers of a ggplot2 plot while simultaneously keeping all labels and text in vector format. This allows users to keep plots within the reasonable size limit without losing vector properties of the scale-sensitive information.

This package provides a toolkit for working with Biological Observation Matrix (BIOM) files. Features include reading/writing all BIOM formats, rarefaction, alpha diversity, beta diversity (including UniFrac), summarizing counts by taxonomic level, and sample subsetting. Standalone functions for reading, writing, and subsetting phylogenetic trees are also provided.

This package converts latitude/longitude into projected coordinates.

This is an R package for imputing dropout events. Many statistical methods in cell type identification, visualization and lineage reconstruction do not account for dropout events. DrImpute can improve the performance of such software by imputing dropout events.

This package creates scatterpie plots, especially useful for plotting pies on a map.

This package provides statistical methods especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Ergonomics and Anthropometry. They are based on clustering, the statistical concept of data depth, statistical shape analysis and archetypal analysis.

This package provides tools to convert the output of utils::getParseData() to an XML tree, that one can search via XPath, and is easier to manipulate in general.

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.

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 implements nested cross-validation applied to the glmnet and caret packages. With glmnet this includes cross-validation of elastic net alpha parameter. A number of feature selection filter functions (t-test, Wilcoxon test, ANOVA, Pearson/Spearman correlation, random forest, ReliefF) for feature selection are provided and can be embedded within the outer loop of the nested CV. Nested CV can be also be performed with the caret package giving access to the large number of prediction methods available in caret.

This package creates a lightweight way to add markdown helpfiles to Shiny apps, using modal dialog boxes, with no need to observe each help button separately.

This package provides interpretability methods to analyze the behavior and predictions of any machine learning model. Implemented methods are:

• Feature importance described by Fisher et al. (2018),

• accumulated local effects plots described by Apley (2018),

• partial dependence plots described by Friedman (2001),

• individual conditional expectation ('ice') plots described by Goldstein et al. (2013) https://doi.org/10.1080/10618600.2014.907095,

• local models (variant of 'lime') described by Ribeiro et. al (2016),

• the Shapley Value described by Strumbelj et. al (2014) https://doi.org/10.1007/s10115-013-0679-x,

• feature interactions described by Friedman et. al https://doi.org/10.1214/07-AOAS148 and tree surrogate models.

The range of functions provided by this package makes it possible to draw highly versatile genomic sequence logos. Features include, but are not limited to, modifying colour schemes and fonts used to draw the logo, generating multiple logo plots, and aiding the visualisation with annotations. Sequence logos can easily be combined with other ggplot2 plots.

This package provides the dyn class interfaces ts, irts, zoo and zooreg time series classes to lm, glm, loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress(), quantreg::rq(), randomForest::randomForest() and other regression functions, allowing those functions to be used with time series including specifications that may contain lags, diffs and missing values.

This package provides useful tools for structural equation modeling.

This package provides an implementation of the Tukey, Mandel, Johnson-Graybill, LBI, Tusell and modified Tukey non-additivity tests.

Read and write feather files, a lightweight binary columnar data store designed for maximum speed.

This package provides procedures for model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects. Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), are supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models.

Phangorn is a package for phylogenetic analysis in R. It supports estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation.

This package provides beanplots, an alternative to boxplot/stripchart/violin plots. It can be used to plot univariate comparison graphs.