This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster.

The hdxmsqc package enables us to analyse and visualise the quality of HDX-MS experiments. Either as a final quality check before downstream analysis and publication or as part of a interative procedure to determine the quality of the data. The package builds on the QFeatures and Spectra packages to integrate with other mass-spectrometry data.

The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint.

BiocSet displays different biological sets in a triple tibble format. These three tibbles are element, set, and elementset. The user has the ability to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet.

This is a package for de novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. It provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. It includes GRanges generation and plotting functions.

r-selectr translates a CSS3 selector into an equivalent XPath expression. This allows you to use CSS selectors when working with the XML package as it can only evaluate XPath expressions. Also provided are convenience functions useful for using CSS selectors on XML nodes. This package is a port of the Python package cssselect.

To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.

Iron is a high level web framework built in and for Rust. It is highly concurrent and can scale horizontally on more machines behind a load balancer or by running more threads on a more powerful machine. Iron avoids the bottlenecks encountered in highly concurrent code by avoiding shared writes and locking in the core framework.

ibus-rime provides the Rime input method engine for IBus. Rime is a lightweight, extensible input method engine supporting various input schemas including glyph-based input methods, romanization-based input methods as well as those for Chinese dialects. It has the ability to compose phrases and sentences intelligently and provide very accurate traditional Chinese output.

Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of a dataset while preserving as much variability as possible. By transforming the original variables into a new set of uncorrelated variables called principal components, PCA helps in identifying patterns and simplifying the complexity of high-dimensional data. The RankPCA package provides a streamlined workflow for performing PCA on datasets containing both categorical and continuous variables. It facilitates data preprocessing, encoding of categorical variables, and computes PCA to determine the optimal number of principal components based on a specified variance threshold. The package also computes composite indices for ranking observations, which can be useful for various analytical purposes. Garai, S., & Paul, R. K. (2023) <doi:10.1016/j.iswa.2023.200202>.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

This crate provides a safe wrapper around the native libudev library. It applies the RAII pattern and Rust lifetimes to ensure safe usage of all libudev functionality. The RAII pattern ensures that all acquired resources are released when they're no longer needed, and Rust lifetimes ensure that resources are released in a proper order.

This package provides a tiny low-level library that provides platform-specific standard locations of directories for config, cache and other data on Linux, Windows, macOS and Redox by leveraging the mechanisms defined by the XDG base/user directory specifications on Linux, the Known Folder API on Windows, and the Standard Directory guidelines on macOS.

Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.

Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99â 115.

To facilitate building custom dashboards on the Crunch data platform <https://crunch.io/>, the crunchy package provides tools for working with shiny'. These tools include utilities to manage authentication and authorization automatically and custom stylesheets to help match the look and feel of the Crunch web application. The package also includes several gadgets for use in RStudio'.

Dependent censoring regression models for survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2019) <doi:10.1002/bimj.201800391>.

This package provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <arXiv:1905.05389>.

This package provides a system to facilitate designing comparative (and non-comparative) experiments using the grammar of experimental designs <https://emitanaka.org/edibble-book/>. An experimental design is treated as an intermediate, mutable object that is built progressively by fundamental experimental components like units, treatments, and their relation. The system aids in experimental planning, management and workflow.

Extends the fitdist() (from fitdistrplus') adding the Anderson-Darling ad.test() (from ADGofTest') and Kolmogorov Smirnov Test ks.test() inside, trying the distributions from stats package by default and offering a second function which uses mixed distributions to fit, this distributions are split with unsupervised learning, with Mclust() function (from mclust').

Read Swiss time series data from the KOF Data API, <https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the KOF Datenservice API. The kofdata package is able to consume public information as well as data that requires an API token.

The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.

Software to aid in modeling and analyzing mass-spectrometry-based proteome melting data. Quantitative data is imported and normalized and thermal behavior is modeled at the protein level. Methods exist for normalization, modeling, visualization, and export of results. For a general introduction to MS-based thermal profiling, see Savitski et al. (2014) <doi:10.1126/science.1255784>.