Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.

Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.

Read data from LimeSurvey (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by limer (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.

Carries out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.

This package provides functions for regional frequency analysis using the methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments".

Set up, run and explore the outputs of the Length-based Multi-species model (LeMans; Hall et al. 2006 <doi:10.1139/f06-039>), focused on the marine environment.

Client for programmatic access to the Lake Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions for accessing lake water quality and ecological context data for the US.

This package provides a simple function, mwsApp(), that runs a shiny app spanning multiple, connected windows. This uses all standard shiny conventions, and depends only on the shiny package.

Hidden Markov Models are useful for modeling sequential data. This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning).

An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Biased Random Key Genetic Algorithms.

Statistical methods for analyzing case-control point data. Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.

Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.

R binding for libfswatch', a file system monitoring library. Watch files, or directories recursively, for changes in the background. Log activity, or call an R function, upon every change event.

Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

This package provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. It also provides some helper functions to assist development of other packages.

This package defines data structures for linkage disequilibrium (LD) measures in populations. Its purpose is to simplify handling of existing population-level data for the purpose of flexibly defining LD blocks.

This package is a collection of functions and layers to enhance ggplot2. The flagship function is ggMarginal(), which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

This package provides functions for reading and writing data stored by some versions of Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and Weka and for reading and writing some dBase files.

This package was automatically created by package AnnotationForge version 1.11.21. The probe sequence data was obtained from http://www.affymetrix.com. The file name was AG\_probe\_tab.

This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package.

This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided.