Programs for Martinussen and Scheike (2006), `Dynamic Regression Models for Survival Data', Springer Verlag. Plus more recent developments. Additive survival model, semiparametric proportional odds model, fast cumulative residuals, excess risk models and more. Flexible competing risks regression including GOF-tests. Two-stage frailty modelling. PLS for the additive risk model. Lasso in the ahaz package.

Implementation of two transportation problem algorithms. 1. North West Corner Method 2. Minimum Cost Method or Least cost method. For more technical details about the algorithms please refer below URLs. <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>. <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.

This package provides tools for simulating and modeling traffic flow on road networks using spatial conditional autoregressive (CAR) models. The package represents road systems as graphs derived from OpenStreetMap data <https://www.openstreetmap.org/> and supports network-based spatial dependence, basic preprocessing, and visualization for spatial traffic analysis.

Converting text to numerical features requires specifically created procedures, which are implemented as steps according to the recipes package. These steps allows for tokenization, filtering, counting (tf and tfidf) and feature hashing.

An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the gbm package originally developed by Greg Ridgeway.

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.

Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.

Overall predictive performance is measured by a mean score (or loss), which decomposes into miscalibration, discrimination, and uncertainty components. The main focus is visualization of these distinct and complementary aspects in joint displays. See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).

Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by Mplus and lavaan'. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.; Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.

Pacote para a analise de experimentos com um ou dois fatores com testemunhas adicionais conduzidos no delineamento inteiramente casualizado ou em blocos casualizados. "Package for the analysis of one or two-way experiments with additional controls conducted in a completely randomized design or in a randomized block design".

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

Some tools for cleaning up messy Excel files to be suitable for R. People who have been working with Excel for years built more or less complicated sheets with names, characters, formats that are not homogeneous. To be able to use them in R nowadays, we built a set of functions that will avoid the majority of importation problems and keep all the data at best.

Extension of the tidyverse for SpatRaster and SpatVector objects of the terra package. It includes also new geom_ functions that provide a convenient way of visualizing terra objects with ggplot2'.

Doubly-robust, non-parametric estimators for the transported average treatment effect from Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the intent-to-treatment average treatment effect from Rudolph and van der Laan (2017) <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are estimated using the Super Learner algorithm.

Cleans spectrophotometry data obtained from the Denovix instrument. The package also provides an option to normalize the data in order to compare the quality of the samples obtained.

This is a collection of functions optimized for working with with various kinds of text matrices. Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a Matrix package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.

It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).

This package provides a novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. It also simultaneously performs variable selection for each autoregressive model and hence the order selection.

Deciphering hierarchy of agents exhibiting observable dominance events is a crucial problem in several disciplines, in particular in behavioural analysis of social animals, but also in social sciences and game theory. This package implements an inference approach based on graph theory, namely to extract the optimal acyclic subset of a weighted graph of dominance; this allows for hierarchy estimation through topological sorting. The package also contains infrastructure to investigate partially defined hierarchies and hierarchy dynamics.

This package provides functions for density, cumulative density, quantile and simulation of Tukey g-and-h (1977) distributions. The quantile-based transformation (Hoaglin 1985 <doi:10.1002/9781118150702.ch11>) and its reverse transformation, as well as the letter-value based estimates (Hoaglin 1985), are also provided.

Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.