Smoothing splines with penalties on order m derivatives.

In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.

Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.

This package provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

This package provides a function to convert PRQL strings to SQL strings. Combined with other R functions that take SQL as an argument, PRQL can be used on R.

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

This package implements conformal prediction methods for constructing prediction intervals (regression) and prediction sets (classification) with finite-sample coverage guarantees. Methods include split conformal, CV+ and Jackknife+ (Barber et al. 2021) <doi:10.1214/20-AOS1965>, Conformalized Quantile Regression (Romano et al. 2019) <doi:10.48550/arXiv.1905.03222>, Adaptive Prediction Sets (Romano, Sesia, Candes 2020) <doi:10.48550/arXiv.2006.02544>, Regularized Adaptive Prediction Sets (Angelopoulos et al. 2021) <doi:10.48550/arXiv.2009.14193>, Mondrian conformal prediction for group-conditional coverage (Vovk, Gammerman, and Shafer 2005) <doi:10.1007/b106715>, weighted conformal prediction for covariate shift (Tibshirani et al. 2019) <doi:10.48550/arXiv.1904.06019>, and adaptive conformal inference for sequential prediction (Gibbs and Candes 2021) <doi:10.48550/arXiv.2106.00170>. All methods are distribution-free and provide calibrated uncertainty quantification without parametric assumptions. Works with any model that can produce predictions from new data, including lm', glm', ranger', xgboost', and custom user-defined models.

This package provides an automated framework for penalized regression analysis using Ridge Regression, Lasso Regression and Elastic Net Regression. The package performs data standardization, training-testing data partitioning, cross-validation for hyperparameter tuning, model fitting, coefficient estimation, variable importance assessment, prediction, and performance evaluation. It simplifies regularized regression analysis by integrating the complete modeling workflow into a single function suitable for researchers for better understanding of the data.The methods are based on Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, and Friedman et al. (2010) <doi:10.18637/jss.v033.i01>.

Optimization of conditional inference trees from the package party for classification and regression. For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. In version 2, additionally structured sampling is implemented in functions PrInDTCstruc() and PrInDTRstruc(). In these functions, repeated measurements data can be analyzed, too. Moreover, multilabel 2-stage versions of classification and regression trees are implemented in functions C2SPrInDT() and R2SPrInDT() as well as interdependent multilabel models in functions SimCPrInDT() and SimRPrInDT(). Finally, for mixtures of classification and regression models functions Mix2SPrInDT() and SimMixPrInDT() are implemented. Most of these extensions of PrInDT are described in Buschfeld & Weihs (2026). References: -- Buschfeld, S., Weihs, C. (2026) "Optimizing decision trees for the analysis of World Englishes and sociolinguistic data", Cambridge Elements. <doi:10.1017/9781009470346>; -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" <doi:10.48550/arXiv.2103.02336>; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" <doi:10.48550/arXiv.2103.14931>; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" <doi:10.48550/arXiv.2108.05129>.

An implementation of the Partition Of variation (POV) method as developed by Dr. Thomas A Little <https://thomasalittleconsulting.com> in 1993 for the analysis of semiconductor data for hard drive manufacturing. POV is based on sequential sum of squares and is an exact method that explains all observed variation. It quantitates both the between and within factor variation effects and can quantitate the influence of both continuous and categorical factors.

This package provides a user interface to create or modify pharmacometric models for various modeling and simulation software platforms.

This package produces odds ratio analyses with comprehensive reporting tools. Generates plots, summary tables, and diagnostic checks for logistic regression models fitted with glm() using binomial family. Provides visualisation methods, formatted reporting tables via gt', and tools to assess logistic regression model assumptions.

Extract political party colors and logos from English Wikipedia party pages. Provides functions to scrape party infoboxes for color codes (HEX or HTML color names) and logo images. Includes integration with the Party Facts database for easy party lookups. Designed for political scientists and party researchers working with electoral and party data. For Party Facts, see Döring and Regel (2019) <doi:10.1177/1354068818820671> and Bederke, Döring, and Regel (2023) <doi:10.7910/DVN/TJINLQ>.

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

Tests periodicity in short time series using response surface regression.

Read R package news files, regardless of whether or not the package is installed.

Annotate plots with legends for continuous variables and colour spectra using the base graphics plotting tools; and manipulate irregular polygons. Includes palettes for colour-blind viewers.

The Proton Game is a console-based data-crunching game for younger and older data scientists. Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server. You have to solve four data-based puzzles to find the login and password. There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools. Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try. The knowledge of dplyr is not required but may be very helpful. This game is linked with the ,,Pietraszko's Cave story available at http://biecek.pl/BetaBit/Warsaw. It's a part of Beta and Bit series. You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.

Fit a probabilistic index model as described in Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the modeling function has changed in this new version. The old version is still available at R-Forge.

This is a computational package designed to identify the most sensitive interactions within a network which must be estimated most accurately in order to produce qualitatively robust predictions to a press perturbation. This is accomplished by enumerating the number of sign switches (and their magnitude) in the net effects matrix when an edge experiences uncertainty. The package produces data and visualizations when uncertainty is associated to one or more edges in the network and according to a variety of distributions. The software requires the network to be described by a system of differential equations but only requires as input a numerical Jacobian matrix evaluated at an equilibrium point. This package is based on Koslicki, D., & Novak, M. (2017) <doi:10.1007/s00285-017-1163-0>.

This package provides a framework for defining pipelines of functions for applying data transformations, model estimation and inverse-transformations, resulting in predicted value generation (or model-scoring) functions that automatically apply the entire pipeline of functions required to go from input to predicted output.

This package contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.

Includes functions and data used in the book "Presenting Statistical Results Effectively", Andersen and Armstrong (2022, ISBN: 978-1446269800). Several functions aid in data visualization - creating compact letter displays for simple slopes, kernel density estimates with normal density overlay. Other functions aid in post-model evaluation heatmap fit statistics for binary predictors, several variable importance measures, compact letter displays and simple-slope calculation. Finally, the package makes available the example datasets used in the book.

This package provides functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.