This package contains the Rook specification and convenience software for building and running Rook applications. A Rook application is an R reference class object that implements a call method or an R closure that takes exactly one argument, an environment, and returns a list with three named elements: the status, the headers, and the body.

This package provides a Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density, Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001) <doi:10.1111/j.0006-341X.2001.00795.x>. This package provides the density function, the gradient of the loglikelihood and a random generator for Davidian curves.

This package provides regression models for grouped and coarse data, under the coarsened at random assumption.

This package provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as the bifurcation structure of any data type.

Phylogenetic clustering (phyloclustering) is an evolutionary continuous time Markov Chain model-based approach to identify population structure from molecular data without assuming linkage equilibrium. The package phyclust provides a convenient implementation of phyloclustering for DNA and SNP data, capable of clustering individuals into subpopulations and identifying molecular sequences representative of those subpopulations. It is designed in C for performance and interfaced with R for visualization.

This is a package to infer transmission trees from a dated phylogeny. It includes methods to simulate and analyze outbreaks. The methodology is described in Didelot et al. (2014) and Didelot et al. (2017).

The feature package contains functions to display and compute kernel density estimates, significant gradient and significant curvature regions. Significant gradient and/or curvature regions often correspond to significant features (e.g. local modes).

This package defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. It provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users.

This package contains a set of functions that extend the cancor function. These functions provide new numerical and graphical outputs. It also includes a regularized extension of the canonical correlation analysis to deal with datasets with more variables than observations.

This package provides qualitatively constrained (regression) smoothing splines via linear programming and sparse matrices.

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

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 provides an easy way to fill an environment with active bindings that call a C++ function.

This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.

The main aim of the pander R package is to provide a minimal and easy tool for rendering R objects into Pandoc's markdown. The package is also capable of exporting/converting complex Pandoc documents (reports) in various ways.

This package computes cell fate bias for multi-lineage single-cell data. It also provides visualization tools for analyzing these biases.

This package supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.

This package extends the ggplot2 plotting system to support network visualization. Inspired by ggtree, ggtangle is designed to work with network associated data.

This package provides a collection of tools for building RAxML supermatrix using PHYLIP or aligned FASTA files. These functions will be useful for building large phylogenies using multiple markers.

This package enables the use of emoji and the Font Awesome glyphs in both base and ggplot2 graphics.

This package implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow filehash databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets.

This package provides an improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100,000 samples) very efficiently.

This package implements a self-organizing map which has application in gene clustering. It provides functions like:

• filtering data by certain floor, ceiling, max/min ratio, and max - min difference;

• normalization of the data;

• get the average distortion measure;

• train a self-organizing map;

• summarize a som object;

• yeast cell cycle.

ACDm is a package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. It creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.