Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.

This package contains functions for analysis and summary of tidal datasets. Also provides access to tidal data collected by the National Oceanic and Atmospheric Administration's Center for Operational Oceanographic Products and Services and the Permanent Service for Mean Sea Level. For detailed description and application examples, see Hill, T.D. and S.C. Anisfeld (2021) <doi:10.6084/m9.figshare.14161202.v1> and Hill, T.D. and S.C. Anisfeld (2015) <doi:10.1016/j.ecss.2015.06.004>.

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>.

Method to perform penalized variance component analysis.

This package provides a Shiny application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using vosonSML'.

R functions are not supposed to print text without giving the user the option to turn the printing off or on using a Boolean verbose in a construct like if(verbose) print(...)'. But this black/white approach is rather rigid, and an approach with shades of gray might be more appropriate in many circumstances.

Multi-precision library that allows to store and operate with arbitrarily big integers without loss of precision. It includes a large list of tools to work with them, like: - Arithmetic and logic operators - Modular-arithmetic operators - Computer Number Theory utilities - Probabilistic primality tests - Factorization algorithms - Random generators of diferent types of integers.

This package provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', glm', coxph', rlm', gam', locfit', lmer', randomForest', etc.).

Craft polished tables and plots in Markdown reports. Simply choose whether to treat your data as counts or metrics, and the package will automatically generate well-designed default tables and plots for you. Boiled down to the basics, with labeling features and simple interactive reports. All functions are tidyverse compatible.

Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.

This package provides a graphical user interface to integrate, visualize and explore results from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid species. The app is meant for interactive use and allows users to optionally upload different sources of information, including gene annotation and alignment files, enabling the exploitation and search for candidate genes in a genome browser. In its current version, VIEWpoly supports inputs from MAPpoly', polymapR', diaQTL', QTLpoly', polyqtlR', GWASpoly', and HIDECAN packages.

This package provides a comprehensive framework for building, evaluating, and visualizing regression models for analyzing viral load and CD4 (Cluster of Differentiation 4) lymphocytes data. It leverages the principles of the tidymodels ecosystem of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org> to offer a user-friendly experience in model development. This package includes functions for data preprocessing, feature engineering, model training, tuning, and evaluation, along with visualization tools to enhance the interpretation of model results. It is specifically designed for researchers in biostatistics, computational biology, and HIV research who aim to perform reproducible and rigorous analyses to gain insights into disease dynamics. The main focus is on improving the understanding of the relationships between viral load, CD4 lymphocytes, and other relevant covariates to contribute to HIV research and the visibility of vulnerable seropositive populations.

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.

This package provides a set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.

Trading Strategies for high Option Volatility environment are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (â The Bible of Options Strategies (2nd ed.)â , 2015, ISBN: 9780133964028). Zura Kakushadze, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large. This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) <doi:10.1002/sim.9619>, which is based on Gaussian variational approximate inference and is computationally efficient.

Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.

Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts.

Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.

This package implements the Variable importance Explainable Elastic Shape Analysis pipeline for explainable machine learning with functional data inputs. Converts training and testing data functional inputs to elastic shape analysis principal components that account for vertical and/or horizontal variability. Computes feature importance to identify important principal components and visualizes variability captured by functional principal components. See Goode et al. (2025) <doi:10.48550/arXiv.2501.07602> for technical details about the methodology.

Debugging pipe chains often consists of viewing the output after each step. This package adds RStudio addins and two functions that allow outputing each or select steps in a convenient way.

Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Data analysis of proteomics experiments by mass spectrometry is supported by this collection of functions mostly dedicated to the analysis of (bottom-up) quantitative (XIC) data. Fasta-formatted proteomes (eg from UniProt Consortium <doi:10.1093/nar/gky1049>) can be read with automatic parsing and multiple annotation types (like species origin, abbreviated gene names, etc) extracted. Initial results from multiple software for protein (and peptide) quantitation can be imported (to a common format): MaxQuant (Tyanova et al 2016 <doi:10.1038/nprot.2016.136>), Dia-NN (Demichev et al 2020 <doi:10.1038/s41592-019-0638-x>), Fragpipe (da Veiga et al 2020 <doi:10.1038/s41592-020-0912-y>), ionbot (Degroeve et al 2021 <doi:10.1101/2021.07.02.450686>), MassChroq (Valot et al 2011 <doi:10.1002/pmic.201100120>), OpenMS (Strauss et al 2021 <doi:10.1038/nmeth.3959>), ProteomeDiscoverer (Orsburn 2021 <doi:10.3390/proteomes9010015>), Proline (Bouyssie et al 2020 <doi:10.1093/bioinformatics/btaa118>), AlphaPept (preprint Strauss et al <doi:10.1101/2021.07.23.453379>) and Wombat-P (Bouyssie et al 2023 <doi:10.1021/acs.jproteome.3c00636>. Meta-data provided by initial analysis software and/or in sdrf format can be integrated to the analysis. Quantitative proteomics measurements frequently contain multiple NA values, due to physical absence of given peptides in some samples, limitations in sensitivity or other reasons. Help is provided to inspect the data graphically to investigate the nature of NA-values via their respective replicate measurements and to help/confirm the choice of NA-replacement algorithms. Meta-data in sdrf-format (Perez-Riverol et al 2020 <doi:10.1021/acs.jproteome.0c00376>) or similar tabular formats can be imported and included. Missing values can be inspected and imputed based on the concept of NA-neighbours or other methods. Dedicated filtering and statistical testing using the framework of package limma <doi:10.18129/B9.bioc.limma> can be run, enhanced by multiple rounds of NA-replacements to provide robustness towards rare stochastic events. Multi-species samples, as frequently used in benchmark-tests (eg Navarro et al 2016 <doi:10.1038/nbt.3685>, Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>), can be run with special options considering such sub-groups during normalization and testing. Subsequently, ROC curves (Hand and Till 2001 <doi:10.1023/A:1010920819831>) can be constructed to compare multiple analysis approaches. As detailed example the data-set from Ramus et al 2016 <doi:10.1016/j.jprot.2015.11.011>) quantified by MaxQuant, ProteomeDiscoverer, and Proline is provided with a detailed analysis of heterologous spike-in proteins.

Generates random data sets including: data.frames, lists, and vectors.