This package implements a general framework for creating dependency graphs using projection as introduced in Fan, Feng and Xia (2019)<arXiv:1501.01617>. Both lasso and sparse additive model projections are implemented. Both Pearson correlation and distance covariance options are available to generate the graph.

This package provides functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at <https://www.pct.bike/>. For an academic paper on the methods, see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.

Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at <http://biecek.pl/R>. Among others you will find here data about housing prices, cancer patients, running times and many others.

Data are partitioned (clustered) into k clusters "around medoids", which is a more robust version of K-means implemented in the function pam() in the cluster package. The PAM algorithm is described in Kaufman and Rousseeuw (1990) <doi:10.1002/9780470316801>. Please refer to the pam() function documentation for more references. Clustered data is plotted as a split heatmap allowing visualisation of representative "group-clusters" (medoids) in the data as separated fractions of the graph while those "sub-clusters" are visualised as a traditional heatmap based on hierarchical clustering.

This package provides a set of datasets and functions used in the book Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'. Datasets either come from real studies or are created to be as similar as possible to real studies.

Measure productivity and efficiency using Data Envelopment Analysis (DEA). Available methods include DEA under different technology assumptions, bootstrapping of efficiency scores and calculation of the Malmquist productivity index. Analyses can be performed either in the console or with the provided shiny app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984) <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.

This package contains the functions for construction and visualization of underlying and reflexivity graphs of the three families of the proximity catch digraphs (PCDs), see (Ceyhan (2005) ISBN:978-3-639-19063-2), and for computing the edge density of these PCD-based graphs which are then used for testing the patterns of segregation and association against complete spatial randomness (CSR)) or uniformity in one and two dimensional cases. The PCD families considered are Arc-Slice PCDs, Proportional-Edge (PE) PCDs (Ceyhan et al. (2006) <doi:10.1016/j.csda.2005.03.002>) and Central Similarity PCDs (Ceyhan et al. (2007) <doi:10.1002/cjs.5550350106>). See also (Ceyhan (2016) <doi:10.1016/j.stamet.2016.07.003>) for edge density of the underlying and reflexivity graphs of PE-PCDs. The package also has tools for visualization of PCD-based graphs for one, two, and three dimensional data.

Streamline the creation of Docker images with R packages and dependencies embedded. The pracpac package provides a usethis'-like interface to creating Dockerfiles with dependencies managed by renv'. The pracpac functionality is described in Nagraj and Turner (2023) <doi:10.48550/arXiv.2303.07876>.

Pupillometric data collected using SR Research Eyelink eye trackers requires significant preprocessing. This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).

Inspired by Moreira and Gamerman (2022) <doi:10.1214/21-AOAS1569>, this methodology expands the idea by including Marks in the point process. Using efficient C++ code, the estimation is possible and made faster with OpenMP <https://www.openmp.org/> enabled computers. This package was developed under the project PTDC/MAT-STA/28243/2017, supported by Portuguese funds through the Portuguese Foundation for Science and Technology (FCT).

Spatial Analysis for exploration of Pakistan Population Census 2017 (<https://www.pbs.gov.pk/content/population-census>). It uses data from R package PakPC2017'.

See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.

This package performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.

Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. panelsummary extends modelsummary (Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from modelsummary to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation.

Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

Computes the Owen's T function or the bivariate normal integral using one of the following: modified Euler's arctangent series, tetrachoric series, or Vasicek's series. For the methods, see Komelj, J. (2023) <doi:10.4236/ajcm.2023.134026> (or reprint <arXiv:2312.00011> with better typography) and Vasicek, O. A. (1998) <doi:10.21314/JCF.1998.015>.

Computes penalized regression calibration (PRC), a statistical method for the dynamic prediction of survival when many longitudinal predictors are available. See Signorelli (2024) <doi:10.32614/RJ-2024-014> and Signorelli et al. (2021) <doi:10.1002/sim.9178> for details.

This package implements principal component analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

The plsdof package provides Degrees of Freedom estimates for Partial Least Squares (PLS) Regression. Model selection for PLS is based on various information criteria (aic, bic, gmdl) or on cross-validation. Estimates for the mean and covariance of the PLS regression coefficients are available. They allow the construction of approximate confidence intervals and the application of test procedures (Kramer and Sugiyama 2012 <doi:10.1198/jasa.2011.tm10107>). Further, cross-validation procedures for Ridge Regression and Principal Components Regression are available.

Procrustes analyses to infer co-phylogenetic matching between pairs of phylogenetic trees.

Graphical methods testing multivariate normality assumption. Methods including assessing score function, and moment generating functions,independent transformations and linear transformations. For more details see Tran (2024),"Contributions to Multivariate Data Science: Assessment and Identification of Multivariate Distributions and Supervised Learning for Groups of Objects." , PhD thesis, <https://our.oakland.edu/items/c8942577-2562-4d2f-8677-cb8ec0bf6234>.