Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.

These experimental expression data (5 leukemic CLL B-lymphocyte of aggressive form from GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the Cascade one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

Analyzes data from a Conconi et al. (1996) <doi:10.1055/s-2007-972887> treadmill fitness test where speed is augmented by a constant amount every set number of seconds to estimate the anaerobic (lactate) threshold speed and heart rate. It reads a TCX file, allows optional removal observations from before and after the actual test, fits a change-point linear model where the change-point is the estimate of the lactate threshold, and plots the data points and fit model. Details of administering the fitness test are provided in the package vignette. Functions work by default for Garmin Connect TCX exports but may require additional data preparation for heart rate, time, and speed data from other sources.

This package provides a set of functions to conduct Conjunctive Analysis of Case Configurations (CACC) as described in Miethe, Hart, and Regoeczi (2008) <doi:10.1007/s10940-008-9044-8>, and identify and quantify situational clustering in dominant case configurations as described in Hart (2019) <doi:10.1177/0011128719866123>. Initially conceived as an exploratory technique for multivariate analysis of categorical data, CACC has developed to include formal statistical tests that can be applied in a wide variety of contexts. This technique allows examining composite profiles of different units of analysis in an alternative way to variable-oriented methods.

This package provides functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.

Analysis of configuration frequencies for simple and repeated measures, multiple-samples CFA, hierarchical CFA, bootstrap CFA, functional CFA, Kieser-Victor CFA, and Lindner's test using a conventional and an accelerated algorithm.

Classifies the type of cancer using routinely collected data commonly found in cancer registries from pathology reports. The package implements the International Classification of Diseases for Oncology, 3rd Edition site (topography), histology (morphology), and behaviour codes of neoplasms to classify cancer type <https://www.who.int/standards/classifications/other-classifications/international-classification-of-diseases-for-oncology>. Classification in children utilize the International Classification of Childhood Cancer by Steliarova-Foucher et al. (2005) <doi:10.1002/cncr.20910>. Adolescent and young adult cancer classification is based on Barr et al. (2020) <doi:10.1002/cncr.33041>.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsaf R-package includes a shiny based interface for an easy application of the cmsafops and cmsafvis packages - the CM SAF R Toolbox. The Toolbox offers an easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates. An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.

This package provides functions for predictor pruning using association-based and model-based approaches. Includes corrPrune() for fast correlation-based pruning, modelPrune() for VIF-based regression pruning, and exact graph-theoretic algorithms (Eppsteinâ Löfflerâ Strash, Bronâ Kerbosch) for exhaustive subset enumeration. Supports linear models, GLMs, and mixed models ('lme4', glmmTMB').

This package provides means of plots for comparing utilization data of compute systems.

We aim to deal with the average treatment effect (ATE), where the data are subject to high-dimensionality and measurement error. This package primarily contains two functions, which are used to generate artificial data and estimate ATE with high-dimensional and error-prone data accommodated.

This package implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>. See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.

This package provides a common misconception is that the Hochberg procedure comes up with adequate overall type I error control when test statistics are positively correlated. However, unless the test statistics follow some standard distributions, the Hochberg procedure requires a more stringent positive dependence assumption, beyond mere positive correlation, to ensure valid overall type I error control. To fill this gap, we formulate statistical tests grounded in rank correlation coefficients to validate fulfillment of the positive dependence through stochastic ordering (PDS) condition. See Gou, J., Wu, K. and Chen, O. Y. (2024). Rank correlation coefficient based tests on positive dependence through stochastic ordering with application in cancer studies, Technical Report.

This package provides datasets containing preformatted maps of Norway at the county, municipality, and ward (Oslo only) level for redistricting in 2024, 2020, 2018, and 2017. Multiple layouts are provided (normal, split, and with an insert for Oslo), allowing the user to rapidly create choropleth maps of Norway without any geolibraries.

Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. (1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm from the MASS package).

Fits a Causal Effect Random Forest of Interaction Tress (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see L. Li, R. A. Levine, and J. Fan (2022) <doi:10.1002/sta4.457>.

This package implements an MCMC algorithm to estimate a hierarchical multinomial logit model with a normal heterogeneity distribution. The algorithm uses a hybrid Gibbs Sampler with a random walk metropolis step for the MNL coefficients for each unit. Dependent variable may be discrete or continuous. Independent variables may be discrete or continuous with optional order constraints. Means of the distribution of heterogeneity can optionally be modeled as a linear function of unit characteristics variables.

Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>.

Every research team have their own script for calculation of hemodynamic indexes. This package makes it possible to insert a long-format dataframe, and add both periods of interest (trigger-periods), and delete artifacts with deleter-files.

This package provides tools for working with the International Classification of Diseases ('ICD-10 Chile official MINSAL'/'DEIS v2018). Includes optimized SQL search with SQLite', fuzzy matching of medical terms (Jaro-Winkler), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS <https://deis.minsal.cl/centrofic/>.

Datasets related to the Comrades Marathon used in the book Antony Unwin (2024, ISBN:978-0367674007) "Getting (more out of) Graphics". The main dataset contains the times of every runner that finished in the time limit for each year the race was run.

This package creates an HTML vertical timeline from a data frame as an input for rmarkdown documents and shiny applications.