This is an external ExperimentData package for LRcell. This data package contains the gene enrichment scores calculated from scRNA-seq dataset which indicates the gene enrichment of each cell type in certain brain region. LRcell package is used to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. For more details, please visit: https://github.com/marvinquiet/LRcell.
This module implements the Rijndael cipher which has been selected as the Advanced Encryption Standard. The keysize for Rijndael is 32 bytes. The blocksize is 16 bytes (128 bits). The supported encryption modes are:
MODE_CBC---Cipher Block ChainingMODE_CFB---Cipher feedbackMODE_CTR---Counter modeMODE_ECB---Electronic cookbook modeMODE_OFB---Output feedback
Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package.
The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.
This RStudio addin makes the creation of Shiny and ShinyDashboard apps more efficient. Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Hickory DNS Server is a safe and secure DNS server with DNSSEC support. Eventually this could be a replacement for BIND9. The DNSSEC support allows for live signing of all records, in it does not currently support records signed offline. The server supports dynamic DNS with SIG0 authenticated requests. Hickory DNS is based on the Tokio and Futures libraries, which means it should be easily integrated into other software that also use those libraries.
R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered.
Implementation of the Future API <doi:10.32614/RJ-2021-048> on top of the batchtools package. This allows you to process futures, as defined by the future package, in parallel out of the box, not only on your local machine or ad-hoc cluster of machines, but also via high-performance compute ('HPC') job schedulers such as LSF', OpenLava', Slurm', SGE', and TORQUE / PBS', e.g. y <- future.apply::future_lapply(files, FUN = process)'.
Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.
The aim is to take in data.frame inputs and utilises methods, such as recursive feature engineering, to enable the features to be removed. What this does differently from the other packages, is that it gives you the choice to remove the variables manually, or it automated this process. Feature selection is a concept in machine learning, and statistical pipelines, whereby unimportant, or less predictive variables are eliminated from the analysis, see Boughaci (2018) <doi:10.1007/s40595-018-0107-y>.
Implement tableGrob object as a clickable image map. The clickableImageMap package is designed to be more convenient and more configurable than the edit() function. Limitations that I have encountered with edit() are cannot control (1) positioning (2) size (3) appearance and formatting of fonts In contrast, when the table is implemented as a tableGrob', all of these features are controllable. In particular, the ggplot2 grid system allows exact positioning of the table relative to other graphics etc.
Arithmetic operations scalar multiplication, addition, subtraction, multiplication and division of LR fuzzy numbers (which are on the basis of extension principle) have a complicate form for using in fuzzy Statistics, fuzzy Mathematics, machine learning, fuzzy data analysis and etc. Calculator for LR Fuzzy Numbers package relieve and aid applied users to achieve a simple and closed form for some complicated operator based on LR fuzzy numbers and also the user can easily draw the membership function of the obtained result by this package.
The XCB util module provides a number of libraries which sit on top of libxcb, the core X protocol library, and some of the extension libraries. These experimental libraries provide convenience functions and interfaces which make the raw X protocol more usable. Some of the libraries also provide client-side code which is not strictly part of the X protocol but which has traditionally been provided by Xlib.
The XCB util-renderutil module provides the following library:
- renderutil: Convenience functions for the Render extension.
The packages provides position specific weight matrices (PWMs) for 303 human serine/threonine and 93 tyrosine kinases originally published in Johnson et al. 2023 (doi:10.1038/s41586-022-05575-3) and Yaron-Barir et al. 2024 (doi:10.1038/s41586-024-07407-y). The package includes basic functionality to score user provided phosphosites. It also includes pre-computed PWM scores ("background scores") for a large collection of curated human phosphosites which can be used to rank PWM scores relative to the background scores ("percentile rank").
Generative Adversarial Networks are applied to generate generative data for a data source. A generative model consisting of a generator and a discriminator network is trained. During iterative training the distribution of generated data is converging to that of the data source. Direct applications of generative data are the created functions for data evaluation, missing data completion and data classification. A software service for accelerated training of generative models on graphics processing units is available. Reference: Goodfellow et al. (2014) <doi:10.48550/arXiv.1406.2661>.
With this package you can build a Storable instance of a record type from Storable instances of its elements in an elegant way. It does not do any magic, just a bit arithmetic to compute the right offsets, that would be otherwise done manually or by a preprocessor like C2HS. There is no guarantee that the generated memory layout is compatible with that of a corresponding C struct. However, the module generates the smallest layout that is possible with respect to the alignment of the record elements.
Interactive visualization of effects, response functions and marginal effects for different kinds of regression models. In this version linear regression models, generalized linear models, generalized additive models and linear mixed-effects models are supported. Major features are the interactive approach and the handling of the effects of categorical covariates: if two or more factors are used as covariates every combination of the levels of each factor is treated separately. The automatic calculation of marginal effects and a number of possibilities to customize the graphical output are useful features as well.
Nested Partially Balanced Bipartite Block (NPBBB) designs involve two levels of blocking: (i) The block design (ignoring sub-block classification) serves as a partially balanced bipartite block (PBBB) design, and (ii) The sub-block design (ignoring block classification) also serves as a PBBB design. More details on constructions of the PBBB designs and their characterization properties are available in Vinayaka et al.(2023) <doi:10.1080/03610926.2023.2251623>. This package calculates A-efficiency values for both block and sub-block structures, along with all parameters of a given NPBBB design.
This package provides a set of tools to analyze and visualize the relationships between host-associated microbiomes of hybrid organisms and those of their progenitor species. Though not necessary, installing the microViz package is recommended as a check for phyloseq objects. To install microViz from R Universe use the following command: install.packages("microViz", repos = c(davidbarnett = "https://david-barnett.r-universe.dev", getOption("repos"))). To install microViz from GitHub use the following commands: install.packages("devtools") followed by devtools::install_github("david-barnett/microViz").
This package implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.
This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.
This package provides a metric expressing the quality of a UMAP layout. This is a package that contains the Saturn_coefficient() function that reads an input matrix, its dimensionality reduction produced by UMAP, and evaluates the quality of this dimensionality reduction by producing a real value in the [0; 1] interval. We call this real value Saturn coefficient. A higher value means better dimensionality reduction; a lower value means worse dimensionality reduction. Reference: Davide Chicco et al. "The Saturn coefficient for evaluating the quality of UMAP dimensionality reduction results" (2025, in preparation).
This package provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B <doi:10.1093/jrsssb/qkae119>. A tutorial for this package can be found at <https://www.laylaparast.com/survivalsurrogate> and a Shiny App implementing the package can be found at <https://parastlab.shinyapps.io/survivalsurrogateApp/>.