Routine for fitting regression models for binary rare events with linear and nonlinear covariate effects when using the quantile function of the Generalized Extreme Value random variable.

Two-step feature-based clustering method designed for micro panel (longitudinal) data with the artificial panel data generator. See Sobisek, Stachova, Fojtik (2018) <arXiv:1807.05926>.

Routines to fit generalized linear models with constrained coefficients, along with inference on the coefficients. Designed to be used in conjunction with the base glm() function.

This package contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

Extra strength glue for data-driven templates. String interpolation for Shiny apps or R Markdown and knitr'-powered Quarto documents, built on the glue and whisker packages.

Accompanying package of the book Financial Risk Modelling and Portfolio Optimisation with R', second edition. The data sets used in the book are contained in this package.

Given the values of sampled units and selection probabilities the desraj function in the package computes the estimated value of the total as well as estimated variance.

This package provides methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

Tests of comparison of two or more survival curves. Allows for comparison of more than two survival curves whether the proportional hazards hypothesis is verified or not.

This package provides tools that allow developers to write functions for prediction error estimation with minimal programming effort and assist users with model selection in regression problems.

Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).

Use the R console as an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.

Use piping, verbs like group_by and summarize', and other dplyr inspired syntactic style when calculating summary statistics on survey data using functions from the survey package.

Stacked ensemble for regression tasks based on mlr3 framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.

This is a small package to provide consistent tick marks for plotting ggplot2 figures. It provides breaks and labels for ggplot2 without requiring ggplot2 to be installed.

This package provides data frames for forest or tree data structures. You can create forest data structures from data frames and process them based on their hierarchies.

The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.

Create plots and tables in a consistent style with WaSHI (Washington Soil Health Initiative) branding. Use washi to easily style your ggplot2 plots and flextable tables.

This package provides an Interface to Zenodo (<https://zenodo.org>) REST API, including management of depositions, attribution of DOIs by Zenodo and upload and download of files.

This is a package to support identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space.

This package allows building the hierarchy of domains starting from Hi-C data. Each hierarchical level is identified by a minimum value of physical insulation between neighboring domains.

This package provides methods for species distribution modeling, i.e., predicting the environmental similarity of any site to that of the locations of known occurrences of a species.

This package provides tools for reading .xls and .sbj files which are written by the proprietary program z-Tree for developing and carrying out economic experiments.

This package provides functions and data accompanying the second edition of the book "Data Mining with R, learning with case studies" by Luis Torgo, published by CRC Press.