Computes the most important properties of four Bayesian early gating designs (two single arm and two randomized controlled designs), such as minimum required number of successes in the experimental group to make a GO decision, operating characteristics and average operating characteristics with respect to the sample size. These might aid in deciding what design to use for the early phase trial.

Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.

Estimates the conditional error distributions of random forest predictions and common parameters of those distributions, including conditional misclassification rates, conditional mean squared prediction errors, conditional biases, and conditional quantiles, by out-of-bag weighting of out-of-bag prediction errors as proposed by Lu and Hardin (2021). This package is compatible with several existing packages that implement random forests in R.

This package provides tools for model specification in the latent variable framework (add-on to the lava package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

Agricultural data for 1888-2021 from the Morrow Plots at the University of Illinois. The world's second oldest ongoing agricultural experiment, the Morrow Plots measure the impact of crop rotation and fertility treatments on corn yields. The data includes planting information and annual yield measures for corn grown continuously and in rotation with other crops, in treated and untreated soil.

This package provides tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically m-Path Sense <https://m-path.io>. The main task of mpathsenser is therefore to read m-Path Sense JSON files into a database and provide several convenience functions to aid in data processing.

Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the GritBot <https://www.rulequest.com/gritbot-info.html> software.

This package provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.

The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. If all letters are revealed before your guesses run out, you win this game; otherwise you fail. You may run multiple games to guess different words.

NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features.

The scRNAseqApp is a Shiny app package designed for interactive visualization of single-cell data. It is an enhanced version derived from the ShinyCell, repackaged to accommodate multiple datasets. The app enables users to visualize data containing various types of information simultaneously, facilitating comprehensive analysis. Additionally, it includes a user management system to regulate database accessibility for different users.

This package provides grid grobs that fill in a user-defined area with various patterns. It includes enhanced versions of the geometric and image-based patterns originally contained in the ggpattern package as well as original pch, polygon_tiling, regular_polygon, rose, text, wave, and weave patterns plus support for custom user-defined patterns.

This package provides routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em(), haplo.glm(), haplo.score(), and haplo.power(); all of which have detailed examples in the vignette.

When creating a package, authors may sometimes struggle with coming up with easy and straightforward function names, and at the same time hoping that other packages do not already have the same function names. In trying to meet this goal, sometimes, function names are not descriptive enough and may confuse the potential users. The purpose of this package is to serve as a package function short form generator and also provide shorthand names for other functions. Having this package will entice authors to create long function names without the fear of users not wanting to use their packages because of the long names. In a way, everyone wins - the authors can use long descriptive function names, and the users can use this package to make short functions names while still using the package in question.

Streamlining the clustering and visualization of time-series gene expression data from RNA-Seq experiments, this tool supports fuzzy c-means and k-means clustering algorithms. It is compatible with outputs from widely-used packages such as Seurat', Monocle', and WGCNA', enabling seamless downstream visualization and analysis. See Lokesh Kumar and Matthias E Futschik (2007) <doi:10.6026/97320630002005> for more details.

R API client package for Fingrid Open Data <https://data.fingrid.fi/> on the electricity market and the power system. get_data() function holds the main application logic to retrieve time-series data. API calls require free user account registration. Data is made available by Fingrid Oyj and distributed under Creative Commons 4.0 <https://creativecommons.org/licenses/by/4.0/>.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

This package provides a causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information.

An implementation of some of the core network package functionality based on a simplified data structure that is faster in many research applications. This package is designed for back-end use in the statnet family of packages, including EpiModel'. Support is provided for binary and weighted, directed and undirected, bipartite and unipartite networks; no current support for multigraphs, hypergraphs, or loops.

Handles truncated members from the exponential family of probability distributions. Contains functions such as rtruncnorm() and dtruncpois(), which are truncated versions of rnorm() and dpois() from the stats package that also offer richer output containing, for example, the distribution parameters. It also provides functions to retrieve the original distribution parameters from a truncated sample by maximum-likelihood estimation.

Helper functions for empirical research in financial economics, addressing a variety of topics covered in Scheuch, Voigt, and Weiss (2023) <doi:10.1201/b23237>. The package is designed to provide shortcuts for issues extensively discussed in the book, facilitating easier application of its concepts. For more information and resources related to the book, visit <https://www.tidy-finance.org/r/index.html>.