This package processes accelerometer data from uni-axial and tri-axial devices and generates data summaries. Also, includes functions to plot, analyze, and simulate accelerometer data.

This package provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

This package provides a robust backfitting algorithm for additive models based on (robust) local polynomial kernel smoothers. It includes both bounded and re-descending (kernel) M-estimators, and it computes predictions for points outside the training set if desired. See Boente, Martinez and Salibian-Barrera (2017) <doi:10.1080/10485252.2017.1369077> and Martinez and Salibian-Barrera (2021) <doi:10.21105/joss.02992> for details.

This package provides tools for RFM (recency, frequency and monetary value) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a shiny app for interactive segmentation. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

Statistical inference for the regression coefficients in high-dimensional linear models with hidden confounders. The Doubly Debiased Lasso method was proposed in <arXiv:2004.03758>.

The goal of dlr is to provide a friendly wrapper around the common pattern of downloading a file if that file does not already exist locally.

This package provides select, insert, update, upsert, and delete database operations. Supports PostgreSQL', MySQL', SQLite', and more, and plays nicely with the DBI package.

Count regression models for underdispersed small counts (lambda < 20) based on the three-parameter exponentially weighted Poisson distribution of Ridout & Besbeas (2004) <DOI:10.1191/1471082X04st064oa>.

This package provides tools to set up, train, store, load, investigate and analyze generative neural networks. In particular, functionality for generative moment matching networks is provided.

This package provides functions for calculating the hazard discrimination summary and its standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.

This package provides a spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.

This package provides a collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.

This package provides functions/methods to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Ruediger Frey, and Paul Embrechts.

Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, <https://www.aclweb.org/anthology/D07-1090/>).

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

This package contains an S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors.

This package provides pure R tools to read BMP format images. It is currently limited to 8 bit greyscale images and 24, 32 bit (A)RGB images.

This package contains R functions and datasets detailed in the book "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.

This is a package for exploratory graphical analysis of multivariate data, specifically gene expression data with different projection methods: principal component analysis, correspondence analysis, spectral map analysis.

This package implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

Data cloud geometry (DCG) applies random walks in finding community structures for social networks. Fushing, VanderWaal, McCowan, & Koehl (2013) (<doi:10.1371/journal.pone.0056259>).