Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse or having many missing values) matrix X as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted implicit-feedback model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the weighted-lambda-regularization model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with implicit features (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.

An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.

This package contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.

Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.

This package provides functions for calculating the OPTICS Cordillera. The OPTICS Cordillera measures the amount of clusteredness in a numeric data matrix within a distance-density based framework for a given minimum number of points comprising a cluster, as described in Rusch, Hornik, Mair (2018) <doi:10.1080/10618600.2017.1349664>. We provide an R native version with methods for printing, summarizing, and plotting the result.

This package provides a device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed.

Shiny app for creating interactive consort flow diagrams and other types of flow diagrams, see Moher, Schulz and Altman (2001) <doi:10.1016/S0140-6736(00)04337-3>.

This package provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

Significance test of Spearman's Rho or Kendall's Tau between short-range dependent random variables.

Create CUSUM (cumulative sum) statistics from a vector or dataframe. Also create single or faceted CUSUM control charts, with or without control limits. Accepts vector, dataframe, tibble or data.table inputs.

This package provides a wrapper for the EZC3D library to work with C3D motion capture data.

This is an R implementation of Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure (DIFFEE). The DIFFEE algorithm can be used to fast estimate the differential network between two related datasets. For instance, it can identify differential gene network from datasets of case and control. By performing data-driven network inference from two high-dimensional data sets, this tool can help users effectively translate two aggregated data blocks into knowledge of the changes among entities between two Gaussian Graphical Model. Please run demo(diffeeDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.

This package provides a system for extracting news from Chilean media, specifically through Web Scapping from Chilean media. The package allows for news searches using search phrases and date filters, and returns the results in a structured format, ready for analysis. Additionally, it includes functions to clean the extracted data, visualize it, and store it in databases. All of this can be done automatically, facilitating the collection and analysis of relevant information from Chilean media.

An anonymization algorithm to resist neighbor label attack in a dynamic network.

Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution. The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) <arXiv:2103.09872>).

Fit of a double additive location-scale model with a nonparametric error distribution from possibly right- or interval censored data. The additive terms in the location and dispersion submodels, as well as the unknown error distribution in the location-scale model, are estimated using Laplace P-splines. For more details, see Lambert (2021) <doi:10.1016/j.csda.2021.107250>.

Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).

The load estimation method is based on a general factor model to solve the estimates of load and specific variance. The philosophy of the package is described in Guangbao Guo. (2022). <doi:10.1007/s00180-022-01270-z>.

Various utilities for the Davies distribution.

This package provides a zero dependency package containing functions to declare labels and missing values, coupled with associated functions to create (weighted) tables of frequencies and various other summary measures. Some of the base functions have been rewritten to make use of the specific information about the missing values, most importantly to distinguish between empty NA and declared NA values. Some functions have similar functionality with the corresponding ones from packages "haven" and "labelled". The aim is to ensure as much compatibility as possible with these packages, while offering an alternative in the objects of class "declared".

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.

Solves ordinary and delay differential equations, where the objective function is written in either R or C. Suitable only for non-stiff equations, the solver uses a Dormand-Prince method that allows interpolation of the solution at any point. This approach is as described by Hairer, Norsett and Wanner (1993) <ISBN:3540604529>. Support is also included for iterating difference equations.

This package provides a toolkit for parsing dice notation, analyzing rolls, calculating success probabilities, and plotting outcome distributions.