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.

Data files and a few functions used in the book Linear Models and Regression with R: An Integrated Approach by Debasis Sengupta and Sreenivas Rao Jammalamadaka (2019).

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.

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.

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.

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 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 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 package provides a package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files).

The atSNP package performs affinity tests of motif matches with the SNP (single nucleotide polymorphism) or the reference genomes and SNP-led changes in motif matches.

This package provides useful functions to edit ggplot object (e.g., setting fonts for theme and layers, adding rounded rectangle as background for each of the legends).

This package provides methods and functions for fitting maximum likelihood models in R. This package modifies and extends the mle classes in the stats4 package.

Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches).

This package provides functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, and more.

This package provides fast and memory efficient methods for truncated singular and eigenvalue decompositions, as well as for principal component analysis of large sparse or dense matrices.

Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

This package contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.

An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package RWeka contains the interface code, the Weka jar is in a separate package RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.

This package provides a set of tools for working with Romanian personal numeric codes. The core is a validation function which applies several verification criteria to assess the validity of numeric codes. This is accompanied by functionality for extracting the different components of a personal numeric code. A personal numeric code is issued to all Romanian residents either at birth or when they obtain a residence permit.