This package provides a multivariate weather generator for daily climate variables based on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). It uses a Markov chain for modeling the succession of weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must include the variable Rain and can accept as many other variables as desired.

This package provides tools for fitting and simulating univariate and multivariate damped random walk processes, also known as Ornstein-Uhlenbeck processes or first-order continuous-time autoregressive models, CAR(1) or CARMA(1, 0). The package supports irregularly spaced observation times, heteroscedastic measurement errors, missing measurements across multivariate time series, and polynomial mean trends in normalized time. The current implementation models up to ten time series jointly. Kalman filtering is used to evaluate the likelihood efficiently. Polynomial mean coefficients are handled conditionally within the Kalman recursion, reducing the dimension of numerical maximum likelihood optimization and permitting exact Gaussian conditional updates during Bayesian posterior sampling. Maximum likelihood estimation is computationally efficient and suitable for large-scale data analysis, while Bayesian posterior sampling is better suited to small-scale analyses requiring more careful uncertainty quantification. Users should preserve sufficient numerical precision when loading astronomical observation times; see the manual for details. Also see Hu and Tak (2020) <doi:10.3847/1538-3881/abc1e2>.

Perform fast functional enrichment on feature lists (like genes or proteins) using the hypergeometric distribution. Tailored for speed, this package is ideal for interactive platforms such as Shiny. It supports the retrieval of functional data from sources like GO, KEGG, Reactome, Bioplanet and WikiPathways. By downloading and preparing data first, it allows for rapid successive tests on various feature selections without the need for repetitive, time-consuming preparatory steps typical of other packages.

Generates phylogenetic trees and distance matrices from a list of taxon names, or from a higher taxon expanded down to a chosen lower rank. Trees are obtained as induced subtrees of the Open Tree of Life synthetic tree using the rotl package (Michonneau, Brown and Winter, 2016, <doi:10.1111/2041-210X.12593>). Expansion of a higher taxon to its descendants uses taxize (Chamberlain and Szocs, 2013, <doi:10.12688/f1000research.2-191.v2>).

Compute the fixed effects dynamic panel threshold model suggested by Ramà rez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.

This package implements an explicit exploration strategy for evolutionary algorithms in order to have a more effective search in solving optimization problems. Along with this exploration search strategy, a set of four different Estimation of Distribution Algorithms (EDAs) are also implemented for solving optimization problems in continuous domains. The implemented explicit exploration strategy in this package is described in Salinas-Gutiérrez and Muñoz Zavala (2023) <doi:10.1016/j.asoc.2023.110230>.

This package provides methods and tools for the analysis of Genome Wide Identity-by-Descent ('gwid') mapping data, focusing on testing whether there is a higher occurrence of Identity-By-Descent (IBD) segments around potential causal variants in cases compared to controls, which is crucial for identifying rare variants. To enhance its analytical power, gwid incorporates a Sliding Window Approach, allowing for the detection and analysis of signals from multiple Single Nucleotide Polymorphisms (SNPs).

Penalized regression for generalized linear models for measurement error problems (aka. errors-in-variables). The package contains a version of the lasso (L1-penalization) which corrects for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). It also contains an implementation of the Generalized Matrix Uncertainty Selector, which is a version the (Generalized) Dantzig Selector for the case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).

The Iterative Cumulative Sum of Squares (ICSS) algorithm by Inclan/Tiao (1994) <https://www.jstor.org/stable/2290916> detects multiple change points, i.e. structural break points, in the variance of a sequence of independent observations. For series of moderate size (i.e. 200 observations and beyond), the ICSS algorithm offers results comparable to those obtained by a Bayesian approach or by likelihood ration tests, without the heavy computational burden required by these approaches.

To implement a model-averaging approach with different renewal models, with a primary focus on forecasting large earthquakes. Based on six renewal models (i.e., Poisson, Gamma, Log-Logistics, Weibull, Log-Normal and BPT), model-averaged point estimates are calculated using AIC weights. Additionally, both percentile and studentized bootstrapped model-averaged confidence intervals are constructed. In comparison, point and interval estimation from the individual or "best" model (determined via model selection) can be retrieved.

This package provides functions to access and download data from various NASA APIs <https://api.nasa.gov/#browseAPI>, including: Astronomy Picture of the Day (APOD), Mars Rover Photos, Earth Polychromatic Imaging Camera (EPIC), Near Earth Object Web Service (NeoWs), Earth Observatory Natural Event Tracker (EONET), and NASA Earthdata CMR Search. Most endpoints require a NASA API key for access. Data is retrieved, cleaned for analysis, and returned in a dataframe-friendly format.

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. The Swiss Municipal Data Merger Tool automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state to municipalities of a new state. This functionality is helpful when working with datasets that are based on different spatial references. The package's idea and use case is discussed in the following article: <doi:10.1111/spsr.12487>.

Turns remote file URLs into paths to durable managed local copies stored in a fixed subdirectory of platform-appropriate, package-specific user data directories. Matching copies are reused across R sessions and remain available for offline use. Supports optional ETag-based versions, content validation, and staged replacement that prevents failed downloads and files that fail a supplied validator from becoming managed local copies. Includes tools to inspect, conservatively prune, and explicitly remove retained copies.

This package performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods ('teigen and mclust are currently supported). Can account for mixtures of non-Gaussian distributions via Manly transform (via ManlyMix'). See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023) <doi:10.48550/arXiv.2305.16464>.

rifi analyses data from rifampicin time series created by microarray or RNAseq. rifi is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, rifi detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced.

This package provides functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.

This package provides implementation of methods for estimation of quantitative maps from Multi-Parameter Mapping (MPM) acquisitions including adaptive smoothing methods in the framework of the ESTATICS model. The smoothing method is described in Mohammadi et al. (2017). <doi:10.20347/WIAS.PREPRINT.2432>. Usage of the package is also described in Polzehl and Tabelow (2019), Magnetic Resonance Brain Imaging, Chapter 6, Springer, Use R! Series. <doi:10.1007/978-3-030-29184-6_6>.

Racket is a general-purpose programming language in the Scheme family, with a large set of libraries and a compiler based on Chez Scheme. Racket is also a platform for language-oriented programming, from small domain-specific languages to complete language implementations.

The main Racket distribution comes with many bundled packages, including the DrRacket IDE, libraries for GUI and web programming, and implementations of languages such as Typed Racket, R5RS and R6RS Scheme, Algol 60, and Datalog.

Covers several areas of data processing: batch-splitting, reading and writing of large data files, data tiling, one-hot encoding and decoding of data tiles, stratified proportional (random or probabilistic) data sampling, data normalization and thresholding, substring location and commonalities inside strings, and location and tabulation of amino acids, modifications or associated monoisotopic masses inside modified peptides. The extractor implements code from Matrix.utils', Varrichio C (2020), <https://cran.r-project.org/package=Matrix.utils>.

This package provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.

This package provides a collection of LaTeX styles using Beamer customization for pdf-based presentation slides in RMarkdown'. At present it contains RMarkdown adaptations of the LaTeX themes Metropolis (formerly mtheme') theme by Matthias Vogelgesang and others (now included in TeXLive'), the IQSS them by Ista Zahn (which is included here), and the Monash theme by Rob J Hyndman. Additional (free) fonts may be needed: Metropolis prefers Fira', and IQSS requires Libertinus'.

This package implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.

This package provides curated Global Warming Potential (GWP) values from IPCC Assessment Reports 4 through 6, gas name standardization across documented naming variants (IUPAC/common names, per-report naming changes, and EPA/data.gov naming), and CO2-equivalent conversion utilities. Values are drawn from official IPCC source tables, with corrections applied for two substances where the published table is known to contain errors; see the package vignette for details and source-by-source provenance notes.