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zoo - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.

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16.56 score 3.0k dependents 40k scripts 835k downloads

colorspace - A Toolbox for Manipulating and Assessing Colors and Palettes

Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB, and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with corresponding ggplot2 color scales. Color palette choice is aided by an interactive app (with either a Tcl/Tk or a shiny graphical user interface) and shiny apps with an HCL color picker and a color vision deficiency emulator. Plotting functions for displaying and assessing palettes include color swatches, visualizations of the HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation functions include: desaturation, lightening/darkening, mixing, and simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly). Details can be found on the project web page at <https://colorspace.R-Forge.R-project.org/> and in the accompanying scientific paper: Zeileis et al. (2020, Journal of Statistical Software, <doi:10.18637/jss.v096.i01>).

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15.26 score 2.7k dependents 13k scripts 484k downloads

sandwich - Robust Covariance Matrix Estimators

Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC) covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC) covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators); clustered covariances (one-way and multi-way); panel and panel-corrected covariances; outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>, Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.

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14.62 score 1.0k dependents 10k scripts 297k downloads

partykit - A Toolkit for Recursive Partytioning

A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.

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12.38 score 95 dependents 2.5k scripts 21k downloads

distributions3 - Probability Distributions as S3 Objects

Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

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11.97 score 107 stars 12 dependents 146 scripts 6.4k downloads

ivreg - Instrumental-Variables Regression by '2SLS', '2SM', or '2SMM', with Diagnostics

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

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instrumental-variablesregression-diagnosticstwo-stage-least-squares-regression

10.84 score 25 stars 4 dependents 636 scripts 15k downloads

betareg - Beta Regression

Beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1, see Kosmidis and Zeileis (2025) <doi:10.1093/jrsssc/qlaf039>. For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see Grün, Kosmidis, and Zeileis (2012) <doi:10.18637/jss.v048.i11>.

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quarto

10.57 score 23 dependents 1.6k scripts 21k downloads

exams - Automatic Generation of Exams in R

Automatic generation of exams based on exercises in Markdown or LaTeX format, possibly including R code for dynamic generation of exercise elements. Exercise types include single-choice and multiple-choice questions, arithmetic problems, string questions, and combinations thereof (cloze). Output formats include standalone files (PDF, HTML, Docx, ODT, ...), Moodle XML, QTI 1.2, QTI 2.1, Blackboard, Canvas, OpenOlat, ILIAS, TestVision, Particify, ARSnova, Kahoot!, Grasple, and TCExam. In addition to fully customizable PDF exams, a standardized PDF format (NOPS) is provided that can be printed, scanned, and automatically evaluated.

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9.12 score 1 stars 7 dependents 1.9k scripts 5.2k downloads

crch - Censored Regression with Conditional Heteroscedasticity

Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.

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quarto

8.08 score 3 dependents 143 scripts 5.4k downloads

crch - Censored Regression with Conditional Heteroscedasticity

Different approaches to censored or truncated regression with conditional heteroscedasticity are provided. First, continuous distributions can be used for the (right and/or left censored or truncated) response with separate linear predictors for the mean and variance. Second, cumulative link models for ordinal data (obtained by interval-censoring continuous data) can be employed for heteroscedastic extended logistic regression (HXLR). In the latter type of models, the intercepts depend on the thresholds that define the intervals. Infrastructure for working with censored or truncated normal, logistic, and Student-t distributions, i.e., d/p/q/r functions and distributions3 objects.

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quarto

8.08 score 3 dependents 143 scripts 5.4k downloads

evtree - Evolutionary Learning of Globally Optimal Trees

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The 'evtree' package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the 'partykit' package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.

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cpp

6.70 score 1 dependents 128 scripts 5.4k downloads

glmertree - Generalized Linear Mixed Model Trees

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from 'lme4' and lmtree()/glmtree() from 'partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).

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6.44 score 3 dependents 51 scripts 5.0k downloads

countreg - Count Data Regression

Regression models for count data, including negative binomial, zero-inflated, zero-truncated, and hurdle models. Drivers for combination with flexmix and mboost are also provided. Previously available functions for graphical goodness-of-fit assessment (rootograms etc.) are now provided in the 'topmodels' package on R-Forge.

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5.45 score 6 stars 236 scripts

glmx - Generalized Linear Models Extended

Extended techniques for generalized linear models (GLMs), especially for binary responses, including parametric links and heteroscedastic latent variables.

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5.20 score 4 dependents 51 scripts 5.1k downloads

fortunes - R Fortunes

A collection of fortunes from the R community.

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5.00 score 202 scripts 5.0k downloads

glogis - Fitting and Testing Generalized Logistic Distributions

Tools for the generalized logistic distribution (Type I, also known as skew-logistic distribution), encompassing basic distribution functions (p, q, d, r, score), maximum likelihood estimation, and structural change methods.

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4.32 score 2 dependents 31 scripts 3.7k downloads

exams2forms - Embedding 'exams' Exercises as Forms in 'rmarkdown' or 'quarto' Documents

Automatic generation of quizzes or individual questions as (interactive) forms within 'rmarkdown' or 'quarto' documents based on 'R/exams' exercises.

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3.63 score 142 scripts 295 downloads

topmodels - Infrastructure for Forecasting and Assessment of Probabilistic Models

Unified infrastructure for probabilistic models and distributional regressions: Probabilistic forecasting of in-sample and out-of-sample of probabilities, densities, quantiles, and moments. Probabilistic residuals and scoring via log-score (or log-likelihood), (continuous) ranked probability score, etc. Diagnostic graphics like rootograms, PIT histograms, (randomized) quantile residual Q-Q plots, and reliagrams (reliability diagrams).

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3.62 score 1 dependents 40 scripts

circtree - Regression Trees and Forests for Circular Responses

Infrastructure for fitting distributional trees and forests based on maximum-likelihood estimation of parameters for a circular response, as well as regression methods for a circular response based on maximum-likelihood estimation are provided. For both approaches the von Mises distribution is employed as circular response distribution.

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openmp

3.60 score 1 scripts

model4you - Stratified and Personalised Models Based on Model-Based Trees and Forests

Model-based trees for subgroup analyses in clinical trials and model-based forests for the estimation and prediction of personalised treatment effects (personalised models). Currently partitioning of linear models, lm(), generalised linear models, glm(), and Weibull models, survreg(), is supported. Advanced plotting functionality is supported for the trees and a test for parameter heterogeneity is provided for the personalised models. For details on model-based trees for subgroup analyses see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for details on model-based forests for estimation of individual treatment effects see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.

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2.81 score 16 scripts 387 downloads

brtobit - Bias-Reduced Tobit Regression

Tobit models are regression models with a Gaussian response variable left-censored at zero, constant latent variance, and a latent mean that depends on covariates through a linear predictor. As an alternative to plain maximum likelihood estimation, the adjusted score equations of Kosmidis and Firth (2010) <doi:10.1214/10-ejs579> are utilized to obtain bias-reduced estimates of the model parameters.

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2.62 score

lagsarlmtree - Spatial Lag Model Trees

Model-based linear model trees adjusting for spatial correlation using a simultaneous autoregressive spatial lag, Wagner and Zeileis (2019) <doi:10.1111/geer.12146>.

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2.60 score 3 scripts 217 downloads

RainTyrol - Precipitation Observations and NWP Forecasts from GEFS

Precipitation observations for the month of July in the years 1985-2012 for 95 stations in Tyrol, Austria, obtained from EHYD. Numerical weather prediction (NWP) forecasts from GEFS.

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2.60 score 1 scripts

palmtree - Partially Additive (Generalized) Linear Model Trees

This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the 'partykit' package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2019) <doi:10.1007/s11634-018-0342-1>. The package offers coef(), logLik(), plot(), and predict() functions for PALM trees.

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2.60 score 8 scripts 179 downloads

disttree - Trees and Forests for Distributional Regression

Infrastructure for fitting distributional regression trees and forests based on maximum-likelihood estimation of parameters for specified distribution families, for example from the GAMLSS family.

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2.60 score 6 scripts

c403 - Exam Tools for Department of Statistics (c403), Uni Innsbruck

Support tools for managing lectures and exams at Uni Innsbruck, specifically for automatic generation and evaluation of mathematics and statistics exams.

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1.90 score