Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

This package provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.

Constructs a shiny app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.

This package implements various Gifi methods in a user-friendly way: categorical principal component analysis (princals), multiple correspondence analysis (homals), monotone regression analysis (morals).

Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.

The function jskm() creates publication quality Kaplan-Meier plot with at risk tables below. svyjskm() provides plot for weighted Kaplan-Meier estimator.

This package provides a toolkit for identifying potential mortalities and expelled tags in aquatic acoustic telemetry arrays. Designed for arrays with non-overlapping receivers.

This package provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.

Download Current & Historical Bhavcopy. Get Live Market data from NSE India of Equities and Derivatives (F&O) segment. Data source <https://www.nseindia.com/>.

This package provides a comprehensive set of helpers that streamline data transmission and processing, making it effortless to interact with the OpenAI API.

An R autograding extension for Otter-Grader (<https://otter-grader.readthedocs.io>). It supports grading R scripts, R Markdown documents, and R Jupyter Notebooks.

Reviews other packages during code review by looking at their dependencies, code style, code complexity, and how internally defined functions interact with one another.

This package provides functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.

This package implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models.

The Penn World Table 8.x provides information on relative levels of income, output, inputs, and productivity for 167 countries between 1950 and 2011.

This package provides a set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).

Generalization of the classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.

Generating realizations of a fractal Brownian function on uniform 1D & 2D grid with classic and generic versions of the Voss algorithm (random sequential additions).

XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.

This package provides an interface to the Nexus class library which allows parsing of NEXUS, Newick and other phylogenetic tree file formats. It provides elements of the file that can be used to build phylogenetic objects such as ape's phylo or phylobase's phylo4(d). This functionality is demonstrated with read_newick_phylo() and read_nexus_phylo().

STG is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.

This package provides functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.

Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities.

Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>. Alam, M. A, Calhoun, C. D., Wang Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.