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.

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.

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>.

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.

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 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.

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 is a generic data receiver, mainly for decoding radio transmissions from devices on the 433 MHz, 868 MHz, 315 MHz, 345 MHz and 915 MHz ISM bands.

Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19> for details on applying this package while Neuenschwander et al. (2010) <doi:10.1177/1740774509356002> and Schmidli et al. (2014) <doi:10.1111/biom.12242> explain details on the methodology.

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

mpv is a general-purpose audio and video player. It is a fork of mplayer2 and MPlayer. It shares some features with the former projects while introducing many more.

This package provides a system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) <doi:10.12688/f1000research.5981.1>.

Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).

An efficient and convenient set of functions to perform differential network estimation through the use of alternating direction method of multipliers optimization with a variety of loss functions.

This package provides methods and utilities for causal emergence. Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.