Use phenotype risk scores based on linked clinical and genetic data to study Mendelian disease and rare genetic variants. See Bastarache et al. 2018 <doi:10.1126/science.aal4043>.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

This package provides a function to convert PRQL strings to SQL strings. Combined with other R functions that take SQL as an argument, PRQL can be used on R.

This package provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

Bayesian variable selection for regression models of under-reported count data as well as for (overdispersed) Poisson, negative binomal and binomial logit regression models using spike and slab priors.

This package provides access to material from the book "Processing and Analyzing Financial Data with R" by Marcelo Perlin (2017) available at <https://sites.google.com/view/pafdr/home>.

Reads in multi-part parquet files. Will read in parquet files that have not been previously coalesced into one file. Convenient for reading in moderately sized, but split files.

This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and can be applied on spatial data. It can handle both gridded or un-gridded datasets.

Plots and analyzes time-intensity curve data, such as data from (contrast-enhanced) ultrasound. Values such as peak intensity, time to peak and area under the curve are calculated.

Sometimes you need to split your data and work on the two chunks independently before bringing them back together. Taber allows you to do that with its two functions.

This package provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.

Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms.

This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool.

This is a package to perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113).

This package provides an R interface to HiGHS, an optimization solver. It is designed for solving mixed-integer optimization problems with quadratic or linear objectives and linear constraints.

This package provides syntax highlighting for R source code. Currently it supports LaTeX and HTML output. Source code of other languages is supported via Andre Simon's highlight package.

Servr provides an HTTP server in R to serve static files, or dynamic documents that can be converted to HTML files (e.g., R Markdown) under a given directory.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the RSNNS low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.

Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.

RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings.

Analysis of moderation (ANOMO) method conceptualizes the difference and equivalence tests as a moderation problem to test the difference and equivalence of two means (or two effects in two studies).

Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package MASS'.