Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. Superpc is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.

Simulates individual-based models of agricultural pest management and the evolution of pesticide resistance. Management occurs on a spatially explicit landscape that is divided into an arbitrary number of farms that can grow one of up to 10 crops and apply one of up to 10 pesticides. Pest genomes are modelled in a way that allows for any number of pest traits with an arbitrary covariance structure that is constructed using an evolutionary algorithm in the mine_gmatrix() function. Simulations are then run using the run_farm_sim() function. This package thereby allows for highly mechanistic social-ecological models of the evolution of pesticide resistance under different types of crop rotation and pesticide application regimes.

An exact finite-sample test for whether two groups share a covariance matrix, the omnibus form of the differential-network question. Under the Gaussian null the likelihood-ratio statistic has a distribution given by the real Jacobi ensemble that is free of the unknown common covariance, so a single Monte-Carlo calibration at the identity serves every covariance with no estimate of the nuisance covariance; this is the property that survives the dimension barrier, where estimating the covariance is hardest. The max-type high-dimensional test of Cai, Liu and Xia (2013) <doi:10.1080/01621459.2012.758041> is provided for comparison. A pure-C back-end does the numerics and also backs the Python package regstat'.

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 clean, tidy access to statistical data published by the European Central Bank ('ECB') via the ECB Data Portal API <https://data.ecb.europa.eu>. Covers policy interest rates, EURIBOR', euro exchange rates, harmonised consumer price inflation ('HICP'), euro area yield curves, the euro short-term rate ('ESTR'), monetary aggregates (M1, M2, M3), mortgage and lending rates, GDP, unemployment, and government debt-to-GDP. Each dataset has a dedicated function that abstracts away the underlying SDMX key structure, so users do not need to know series codes. A generic fetcher is also provided for direct access to any of the ECB 100-plus dataflows. Data is downloaded on first use and cached locally for subsequent calls.

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 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 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 package provides programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model.

The goal of vetiver is to provide fluent tooling to version, share, deploy, and monitor a trained model. Functions handle both recording and checking the model's input data prototype, and predicting from a remote API endpoint. The vetiver package is extensible, with generics that can support many kinds of models.

CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages.

This package performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found.

GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait models. This test conditions on the trait, which makes it immune to confounding by unmodeled environmental factors. Population structure is modeled via logistic factors, which are estimated using the `lfa` package.

This package provides large-scale single-cell omics data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language.

The Brazilian Jurimetrics Association (ABJ in Portuguese, see <https://abj.org.br/> for more information) is a non-profit organization which aims to investigate and promote the use of statistics and probability in the study of Law and its institutions. This package has a set of datasets commonly used in our book.

This package provides a set of Boolean operators which accept integers of any size, in any base from 2 to 36, including 2's complement format, and perform actions like "AND," "OR", "NOT", "SHIFTR/L" etc. The output can be in any base specified. A direct base to base converter is included.

This package provides tools to deploy R web server applications that follow the _server.yml standard. This standard allows different R server frameworks ('plumber2', fiery', etc.) to be deployed using a common interface. The package supports deployment to DigitalOcean and includes validation tools to ensure _server.yml files are correctly formatted.

Runs hierarchical linear Bayesian models. Samples from the posterior distributions of model parameters in JAGS (Just Another Gibbs Sampler; Plummer, 2017, <http://mcmc-jags.sourceforge.net>). Computes Bayes factors for group parameters of interest with the Savage-Dickey density ratio (Wetzels, Raaijmakers, Jakab, Wagenmakers, 2009, <doi:10.3758/PBR.16.4.752>).

This package provides a toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm implements the estimation for large networks efficiently building on the lighthergm and hergm packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.

Energy landscape analysis (ELA) is a systematic method for analyzing an energy landscape represented as a weighted network. This R package is especially designed to analyze ecological dynamics, i.e., transitions in community compositions. For details of the analysis framework, please visit Suzuki K et al. (2021) <doi:10.1002/ecm.1469>.

Runs a series of configurable tests against a user's compute environment. This can be used for checking that things like a specific directory or an environment variable is available before you start an analysis. Alternatively, you can use the package's situation report when filing error reports with your compute infrastructure.

Download Data from the FAOSTAT Database of the Food and Agricultural Organization (FAO) of the United Nations. A list of functions to download statistics from FAOSTAT (database of the FAO <https://www.fao.org/faostat/>) and WDI (database of the World Bank <https://data.worldbank.org/>), and to perform some harmonization operations.

Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. The model combines state space methodology with a switching component in the observation equation to allow flexible modeling of complex seasonal patterns, including time-varying effects and multiple seasonalities.

This package provides a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.