This package provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>. It also contains several auxiliary functions for generating sample networks using various network models and graphons.

This package provides a compilation of functions to create visually appealing and information-rich plots of meta-analytic data using ggplot2'. Currently allows to create forest plots, funnel plots, and many of their variants, such as rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with the funnel plot in the context of meta-analysis are provided.

Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>.

Requires rooted phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways. "MonoPhy: a simple R package to find and visualize monophyly issues." Schwery, O. & O'Meara, B.C. (2016) <doi:10.7717/peerj-cs.56>.

Enhances mlexperiments <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: glmnet <https://CRAN.R-project.org/package=glmnet>, ranger <https://CRAN.R-project.org/package=ranger>, xgboost <https://CRAN.R-project.org/package=xgboost>, and lightgbm <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the mlexperiments R package.

An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.

OpenTelemetry is a collection of tools, APIs', and SDKs used to instrument, generate, collect, and export telemetry data (metrics, logs, and traces) for analysis in order to understand your software's performance and behavior. This package contains the OpenTelemetry SDK', and exporters. Use this package to export traces, metrics, logs from instrumented R code. Use the otel package to instrument your R code for OpenTelemetry'.

This package implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).

This package provides a Shiny input widget, pasteBoxInput, that allows users to paste images directly into a Shiny application. The pasted images are captured as Base64 encoded strings and can be used within the application for various purposes, such as display or further processing. This package is particularly useful for applications that require easy and quick image uploads without the need for traditional file selection dialog boxes.

Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.

Fits and evaluates three-state partitioned survival analyses (PartSAs) and Markov models (clock forward or clock reset) to progression and overall survival data typically collected in oncology clinical trials. These model structures are typically considered in cost-effectiveness modeling in advanced/metastatic cancer indications. Muston (2024). "Informing structural assumptions for three state oncology cost-effectiveness models through model efficiency and fit". Applied Health Economics and Health Policy.

Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.

The Wordle game. Players have six attempts to guess a five-letter word. After each guess, the player is informed which letters in their guess are either: anywhere in the word; in the right position in the word. This can be used to inform the next guess. Can be played interactively in the console, or programmatically. Based on Josh Wardle's game <https://www.powerlanguage.co.uk/wordle/>.

Permite obtener rápidamente una serie de medidas de resumen y gráficos para datos numéricos discretos o continuos en series simples. También permite obtener tablas de frecuencia clásicas y gráficos cuando se desea realizar un análisis de series agrupadas. Su objetivo es de aplicación didáctica para un curso introductorio de Bioestadà stica utilizando el software R, para las carreras de grado las carreras de grado y otras ofertas educativas de la Facultad de Ciencias Agrarias de la UNJu / It generates summary measures and graphs for discrete or continuous numerical data in simple series. It also enables the creation of classic frequency tables and graphs when analyzing grouped series. Its purpose is for educational application in an introductory Biostatistics course using the R software, aimed at undergraduate programs and other educational offerings of the Faculty of Agricultural Sciences at the National University of Jujuy (UNJu).

This package provides functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) <doi:10.1111/mec.14324> and Druet and Gautier (2022) <doi:10.1016/j.tpb.2022.03.001>.

Fast tools for unequal probability sampling in multi-dimensional spaces, implemented in Rust for high performance. The package offers a wide range of methods, including Sampford (Sampford, 1967, <doi:10.1093/biomet/54.3-4.499>) and correlated Poisson sampling (Bondesson and Thorburn, 2008, <doi:10.1111/j.1467-9469.2008.00596.x>), pivotal sampling (Deville and Tillé, 1998, <doi:10.1093/biomet/91.4.893>), and balanced sampling such as the cube method (Deville and Tillé, 2004, <doi:10.1093/biomet/91.4.893>) to ensure auxiliary totals are respected. Spatially balanced approaches, including the local pivotal method (Grafström et al., 2012, <doi:10.1111/j.1541-0420.2011.01699.x>), spatially correlated Poisson sampling (Grafström, 2012, <doi:10.1016/j.jspi.2011.07.003>), and locally correlated Poisson sampling (Prentius, 2024, <doi:10.1002/env.2832>), provide efficient designs when the target variable is linked to auxiliary information.

This package can be used to predict the r-species accumulation curve (r-SAC), which is the number of species represented at least r times as a function of the sampling effort. When r = 1, the curve is known as the species accumulation curve, or the library complexity curve in high-throughput genomic sequencing. The package includes both parametric and nonparametric methods, as described by Deng C, et al. (2018).

This package provides HTTP error helpers. Methods are included for general purpose HTTP error handling, as well as individual methods for every HTTP status code, both via status code numbers as well as their descriptive names. It supports the ability to adjust behavior to stop, message or warning. It includes the ability to use a custom whisker template to have any configuration of status code, short description, and verbose message.

This package provides tools to visualize simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams, visualizing webs, electrical networks, etc. It also includes supporting material for the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter M.J. Herman (2009) and the book "Solving Differential Equations in R" by Karline Soetaert, Jeff Cash and Francesca Mazzia (2012).

This package provides a fast implementation of a key-value store. Environments are commonly used as key-value stores, but every time a new key is used, it is added to R's global symbol table, causing a small amount of memory leakage. This can be problematic in cases where many different keys are used. Fastmap avoids this memory leak issue by implementing the map using data structures in C++.

Rofi-pass provides a way to manipulate information stored using password-store through rofi interface:

1. open URLs of entries with hotkey;

2. type any field from entry;

3. auto-typing of user and/or password fields;

4. auto-typing username based on path;

5. auto-typing of more than one field, using the autotype entry;

6. bookmarks mode (open stored URLs in browser, default: Alt+x).

martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork.

This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification.

Combination of results for meta-analysis using significance and effect size only. P-values and fold-change are combined to obtain a global significance on each metabolite. Produces a volcano plot summarising the relevant results from meta-analysis. Vote-counting reports for metabolites. And explore plot to detect discrepancies between studies at a first glance. Methodology is described in the Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.