Explore synesthesia consistency test data, calculate consistency scores, and classify participant data as valid or invalid.

This package provides tools for creating and working with survey replicate weights, extending functionality of the survey package from Lumley (2004) <doi:10.18637/jss.v009.i08>. Implements bootstrap methods for complex surveys, including the generalized survey bootstrap as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>. Methods are provided for applying nonresponse adjustments to both full-sample and replicate weights as described by Rust and Rao (1996) <doi:10.1177/096228029600500305>. Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>. Diagnostic functions are included to compare weights and weighted estimates from different sets of replicate weights.

This package performs canonical correlation for survey data, including multiple tests of significance for secondary canonical correlations. A key feature of this package is that it incorporates survey data structure directly in a novel test of significance via a sequence of simple linear regression models on the canonical variates. See reference - Cruz-Cano, Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" The R Journal under review.

Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>, Amendola et al. (2015) <doi:10.1002/for.2322> and Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.

Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.

The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management. Furthermore, it contains several functions for analysis and visualization of (spatial) infection data.

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

Select best combination of auxiliary variables with certain criterion.

This package provides a critical first step in systematic literature reviews and mining of academic texts is to identify relevant texts from a range of sources, particularly databases such as Web of Science or Scopus'. These databases often export in different formats or with different metadata tags. synthesisr expands on the tools outlined by Westgate (2019) <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats (such as bibtex', ris', or ciw') in a standard way, and allows merging and deduplication of the resulting dataset.

Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.

This package provides a collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>.

Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.

Spatial coverage sampling and random sampling from compact geographical strata created by k-means. See Walvoort et al. (2010) <doi:10.1016/j.cageo.2010.04.005> for details.

Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.

Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).

Generate an invoice containing a header with invoice number and businesses details. The invoice table contains any of: salary, one-liner costs, grouped costs. Under the table signature and bank account details appear. Pages are numbered when more than one. Source .json and .Rmd files are editable in the app. A .csv file with raw data can be downloaded. This package includes functions for getting exchange rates between currencies based on quantmod (Ryan and Ulrich, 2023 <https://CRAN.R-project.org/package=quantmod>).

This package implements a semi-supervised learning framework for finite mixture models under a mixed-missingness mechanism. The approach models both missing completely at random (MCAR) and entropy-based missing at random (MAR) processes using a logisticâ entropy formulation. Estimation is carried out via an Expectationâ -Conditional Maximisation (ECM) algorithm with robust initialisation routines for stable convergence. The methodology relates to the statistical perspective and informative missingness behaviour discussed in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package provides functions for data simulation, model estimation, prediction, and theoretical Bayes error evaluation for analysing partially labelled data under a mixed-missingness mechanism.

The goal of safejoin is to guarantee that when performing joins extra rows are not added to your data. safejoin provides a wrapper around dplyr::left_join that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.

Generate syntax for use with the sparklines package for LaTeX.

This package implements a simple, novel clustering algorithm based on optimizing the silhouette width. See <doi:10.1101/2023.11.07.566055> for details.

An interactive document on the topic of basic statistical analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.

Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.

Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) <doi:10.1080/03610926.2011.560739>.