Seasonal- and calendar adjustment of time series with daily frequency using the DSA approach developed by Ollech, Daniel (2018): Seasonal adjustment of daily time series. Bundesbank Discussion Paper 41/2018.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.

Simple helpers for matrix multiplication on data.frames. These allow for more concise code during low level mathematical operations, and help ensure code is more easily read, understood, and serviced.

This package provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.

Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.

This package implements Procrustes cross-validation method for Principal Component Analysis, Principal Component Regression and Partial Least Squares regression models. S. Kucheryavskiy (2023) <doi:10.1016/j.aca.2023.341096>.

Allows the comparison of data cohorts (DC) against a Counter Factual Model (CFM) and measures the difference in terms of an efficacy parameter. Allows the application of Personalised Synthetic Controls.

Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.

Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.

This package computes standardized mean differences and confidence intervals for multiple data types based on Yang, D., & Dalton, J. E. (2012) <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.

The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions.

This package lets you determine the significance of pre-defined sets of genes with respect to an outcome variable, such as a group indicator, a quantitative variable or a survival time.

This package provides a cross-platform Zip compression library for R. It is a replacement for the zip function, that does not require any additional external tools on any platform.

Rizin is a reverse engineering framework and a set of small command-line utilities, providing a complete binary analysis experience with features like disassembler, hexadecimal editor, emulation, binary inspection, debugger, and more.

An interface to the powerful and fairly complete computer algebra system Maxima'. It can be used to start and control Maxima from within R by entering Maxima commands. Results from Maxima can be parsed and evaluated in R. It facilitates outputting results from Maxima in LaTeX and MathML'. 2D and 3D plots can be displayed directly. This package also registers a knitr'-engine enabling Maxima code chunks to be written in RMarkdown documents.

Allows caching of raw data directly in R code. This allows R scripts and R Notebooks to be shared and re-run on a machine without access to the original data. Cached data is encoded into an ASCII string that can be pasted into R code. When the code is run, the data is automatically loaded from the cached version if the original data file is unavailable. Works best for small datasets (a few hundred observations).

This package performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.

An implementation of the ESS algorithm following Amol Deshpande, Minos Garofalakis, Michael I Jordan (2013) <arXiv:1301.2267>. The ESS algorithm is used for model selection in decomposable graphical models.

This package provides a fast class noise detector which provides noise score for each observations. The package takes advantage of RcppArmadillo to speed up the calculation of distances between observations.

Given a database of previous treatment/placebo estimates, their standard errors and sample sizes, the program calculates a significance criteria and power estimate that takes into account the among trial variation.

Spatial (cross-)covariance and related geostatistical tools: the nonparametric (cross-)covariance function , the spline correlogram, the nonparametric phase coherence function, local indicators of spatial association (LISA), (Mantel) correlogram, (Partial) Mantel test.

The goal of tor (to-R) is to help you to import multiple files from a single directory at once, and to do so as quickly, flexibly, and simply as possible.

Manage, provision and use Virtual Machines pre-configured for R. Develop, test and build package in a clean environment. Vagrant tool and a provider (such as Virtualbox') have to be installed.