This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD).
This package provides a tool that enables in silico identification, integration, and modeling of mRNA features that influence post-transcriptional regulation of gene expression at a transcriptome-wide scale.
Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups.
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
This package implements Roy's bivariate geometric model (Roy (1993) <doi:10.1006/jmva.1993.1065>): joint probability mass function, distribution function, survival function, random generation, parameter estimation, and more.
This package implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package config'.
Process command line arguments, allowing scripts to behave like functions, with well-defined inputs and outputs. Helps make data analysis workflows more modular, and therefore more transparent, flexible, and reliable.
Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.
This package provides a GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.
This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.
Integer programming models to assign students to groups by maximising diversity or topic preferences, and to allocate multi-role teaching workloads while balancing role demand, preferences, fairness, and cohort protection.
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>.
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.
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.
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.
This package creates quasi-proportional Venn diagrams with an arbitrary number of sets. It is related to the old nVennR package, but the algorithm and use have been reworked.
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).
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.
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.
Loads and manages statistical genetics data objects including reference panels, genotypes, LD matrices, annotations, and summary statistics. Follows the statgen specification for use across Python', R', and MATLAB'/'Octave runtimes.
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.