This package implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using TMB', fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>, see also Balboa and de Una-Alvarez (2018) <doi:10.18637/jss.v083.i10>, are included.

R functions are not supposed to print text without giving the user the option to turn the printing off or on using a Boolean verbose in a construct like if(verbose) print(...)'. But this black/white approach is rather rigid, and an approach with shades of gray might be more appropriate in many circumstances.

This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

This package provides a pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics.

This package provides a suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels.

This package provides functions to compare two or more survival curves with:

• The Fleming-Harrington test for right-censored data based on permutations and on counting processes.

• An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.

This package provides functionality to run a number of tasks in the differential expression analysis workflow. This encompasses the most widely used steps, from running various enrichment analysis tools with a unified interface to creating plots and beautifying table components linking to external websites and databases. This streamlines the generation of comprehensive analysis reports.

This package provides tools to support the analysis of RNA-seq expression data or other similar kind of data. It provides exploratory plots to evaluate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. It also supports the analysis of differential expression between two experimental conditions with no parametric assumptions.

Circus is an R package for annotation, analysis and visualization of circRNA data. Users can annotate their circRNA candidates with host genes, gene features they are spliced from, and discriminate between known and yet unknown splice junctions. Circular-to-linear ratios of circRNAs can be calculated, and a number of descriptive plots easily generated.

This package contains a collection of various functions to assist in R programming, such as tools to assist in developing, updating, and maintaining R and R packages, calculating the logit and inverse logit transformations, tests for whether a value is missing, empty or contains only NA and NULL values, and many more.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package provides a Rust implementation of a TAR file reader and writer. This library does not currently handle compression, but it is abstract over all I/O readers and writers. Additionally, great lengths are taken to ensure that the entire contents are never required to be entirely resident in memory all at once.

This package performs the Joint and Individual Variation Explained (JIVE) decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a Bayesian Information Criterion (BIC) selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.

Transform coordinates from a specified source to a specified target map projection. This uses the PROJ library directly, by wrapping the PROJ package which leverages libproj', otherwise the proj4 package. The reproj() function is generic, methods may be added to remove the need for an explicit source definition. If proj4 is in use reproj() handles the requirement for conversion of angular units where necessary. This is for use primarily to transform generic data formats and direct leverage of the underlying PROJ library. (There are transformations that aren't possible with PROJ and that are provided by the GDAL library, a limitation which users of this package should be aware of.) The PROJ library is available at <https://proj.org/>.

This package provides direct access to the ALFRED (<https://alfred.stlouisfed.org>) and FRED (<https://fred.stlouisfed.org>) databases. Its functions return tidy data frames for different releases of the specified time series. Note that this product uses the FRED© API but is not endorsed or certified by the Federal Reserve Bank of St. Louis.

This package provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

Multicenter randomized trials involve the collection and analysis of data from numerous study participants across multiple sites. Outliers may be present. To identify outliers, this package examines data at the individual level (univariate and multivariate) and site-level (with and without covariate adjustment). Methods are outlined in further detail in Rigdon et al (to appear).

This package provides functions for visualizing, animating, solving and analyzing the Rubik's cube. Includes data structures for solvable and unsolvable cubes, random moves and random state scrambles and cubes, 3D displays and animations using OpenGL', patterned cube generation, and lightweight solvers. See Rokicki, T. (2008) <arXiv:0803.3435> for the Kociemba solver.

Semiparametric estimation for censored time series with lower detection limit. The latent response is a sequence of stationary process with Markov property of order one. Estimation of copula parameter(COPC) and Conditional quantile estimation are included for five available copula functions. Copula selection methods based on L2 distance from empirical copula function are also included.

An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.

Create D3 based SVG ('Scalable Vector Graphics') graphics using a simple R API. The package aims to simplify the creation of many SVG plot types using a straightforward R API. The package relies on the r2d3 R package and the D3 JavaScript library. See <https://rstudio.github.io/r2d3/> and <https://d3js.org/> respectively.

This package provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, GLPK', lpSolve', Symphony', and Gurobi are supported as solvers. The gurobi package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.