Get started with new projects by dropping a skeleton of a new project into a new or existing directory, initialise git repositories, and create reproducible environments with the renv package. The package allows for dynamically named files, folders, file content, as well as the functionality to drop individual template files into existing projects.

It fits scale mixture of skew-normal linear mixed models using either an expectationâ maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.

This package provides a constrained two-dimensional Delaunay triangulation package providing both triangulation and generation of voronoi mosaics of irregular spaced data. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license based on a different triangulation algorithm.

This package performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.

The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.

This package provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package.

This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression.

This package analyzes and creates plots of array CGH data. Also, it allows usage of CBS, wavelet-based smoothing, HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data.

This package provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more, there are several utility functions for data handling and management.

This crate contains utility functions for path, file and directory handling. There are multiple main modules for fsio:

1. fsio::path: Holds path related functions and traits.

2. fsio::file: File utility functions such as read_file, write_file, etc.

3. fsio::directory: Directory specific utility functions.

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. rumidas also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

This package provides a single key function, Require that makes rerun-tolerant versions of install.packages and `require` for CRAN packages, packages no longer on CRAN (i.e., archived), specific versions of packages, and GitHub packages. This approach is developed to create reproducible workflows that are flexible and fast enough to use while in development stages, while able to build snapshots once a stable package collection is found. As with other functions in a reproducible workflow, this package emphasizes functions that return the same result whether it is the first or subsequent times running the function, with subsequent times being sufficiently fast that they can be run every time without undue waiting burden on the user or developer.

This package provides functions to accompany the book "Applied Statistical Modeling for Ecologists" by Marc Kéry and Kenneth F. Kellner (2024, ISBN: 9780443137150). Included are functions for simulating and customizing the datasets used for the example models in each chapter, summarizing output from model fitting engines, and running custom Markov Chain Monte Carlo.

Perceptually uniform palettes for commonly used variables in oceanography as functions taking an integer and producing character vectors of colours. See Thyng, K.M., Greene, C.A., Hetland, R.D., Zimmerle, H.M. and S.F. DiMarco (2016) <doi:10.5670/oceanog.2016.66> for the guidelines adhered to when creating the palettes.

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) <doi:10.4310/SII.2013.v6.n2.a1>.

Using the idea of least trimmed square, it could automatically detects and removes outliers from data before estimating the coefficients. It is a robust machine learning tool which can be applied to gene-expression deconvolution technique. Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie (2019) <doi:10.1101/358366>.

Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.

Defines classes and methods that can be used to implement genetic algorithms for feature selection. The idea is that we want to select a fixed number of features to combine into a linear classifier that can predict a binary outcome, and can use a genetic algorithm heuristically to select an optimal set of features.

This package provides functions to help with creating sparklines in the style of Edward Tufte <https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0001OR&topic_id=1> in ggplot2'. It computes ribbon geoms with the interquartile ranges and points and/or labels at the beginning, end, max, and min points.

This package contains many functions useful for monitoring and reporting the results of clinical trials and other experiments in which treatments are compared. LaTeX is used to typeset the resulting reports, recommended to be in the context of knitr'. The Hmisc', ggplot2', and lattice packages are used by greport for high-level graphics.

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the gamlss package <doi:10.32614/CRAN.package.gamlss>.

Holistic generalized linear models (HGLMs) extend generalized linear models (GLMs) by enabling the possibility to add further constraints to the model. The holiglm package simplifies estimating HGLMs using convex optimization. Additional information about the package can be found in the reference manual, the README and the accompanying paper <doi:10.18637/jss.v108.i07>.

We provide a stage-wise selection method using genetic algorithms, designed to efficiently identify main and two-way interactions within high-dimensional linear regression models. Additionally, it implements simulated annealing algorithm during the mutation process. The relevant paper can be found at: Ye, C.,and Yang,Y. (2019) <doi:10.1109/TIT.2019.2913417>.

Synthesize images into characteristic features for time-series analysis or machine learning applications. The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.