This package provides functions to get prediction intervals and prediction points of future observations from any continuous distribution.

The base R data.frame, like any vector, is copied upon modification. This behavior is at odds with that of GUIs and interactive graphics. To rectify this, plumbr provides a mutable, dynamic tabular data model. Models may be chained together to form the complex plumbing necessary for sophisticated graphical interfaces. Also included is a general framework for linking datasets; an typical use case would be a linked brush.

An R6 class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.

This package provides tools for anonymizing sensitive patient and research data. Helps protect privacy while keeping data useful for analysis. Anonymizes IDs, names, dates, locations, and ages while maintaining referential integrity. Methods based on: Sweeney (2002) <doi:10.1142/S0218488502001648>, Dwork et al. (2006) <doi:10.1007/11681878_14>, El Emam et al. (2011) <doi:10.1371/journal.pone.0028071>, Fung et al. (2010) <doi:10.1145/1749603.1749605>.

Kappa, ICC, reliability coefficient, parallel analysis, multi-traits multi-methods, spherical representation of a correlation matrix.

Given a SpatialPolygonsDataFrame and a set of populations for each polygon, compute a population density estimate based on Tobler's pycnophylactic interpolation algorithm. The result is a SpatialGridDataFrame. Methods are described in Tobler Waldo R. (1979) <doi:10.1080/01621459.1979.10481647>.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

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).

R package to compute Incoming Solar Radiation (insolation) for palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and Laskar et al. (2004). Computes daily-mean, season-averaged and annual means and for all latitudes, and polar night dates.

Connect R to the PhotosynQ platform (<https://photosynq.org>). It allows to login and logout, as well as receive project information and project data. Further it transforms the received JSON objects into a data frame, which can be used for the final data analysis.

This package provides classes to pre-process microarray gene expression data as part of the OOMPA collection of packages described at <http://oompa.r-forge.r-project.org/>.

Compute detailed and aggregated performance spectrum for event data. The detailed performance spectrum describes the event data in terms of segments, where the performance of each segment is measured and plotted for any occurrences of this segment over time and can be classified, e.g., regarding the overall population. The aggregated performance spectrum visualises the amount of cases of particular performance over time. Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2018) <doi:10.1007/978-3-319-98648-7_9>.

This package performs sensitivity analysis for publication bias in meta-analyses (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses enable statements such as: "For publication bias to shift the observed point estimate to the null, significant results would need to be at least 30-fold more likely to be published than negative or nonsignificant results." Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. Provides a worst-case meta-analytic point estimate under maximal publication bias obtained simply by conducting a standard meta-analysis of only the negative and "nonsignificant" studies.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.

Wrangle and annotate different types of political texts. It also introduces Urgency Analysis, a new method for the analysis of urgency in political texts.

Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The PAM50 classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The PCAPAM50 package leverages principal component analysis and iterative PAM50 calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.

Computes the Patient-Reported Outcomes (PROs) Joint Contrast (PJC), a residual-based summary that captures information left over after accounting for the clinical Disease Activity index for Psoriatic Arthritis (cDAPSA). PROs (pain and patient global assessment) and joint counts (swollen and tender) are standardized, then each component is adjusted for standardized cDAPSA using natural spline coefficients that were derived from previously published models. The resulting residuals are standardized and combined using fixed principal component loadings, to yield a continuous PJC score and quartile groupings. This package provides a calculator for applying those published coefficients to new datasets; it does not itself estimate spline models or principal components.

This package implements extensions to the projection pursuit tree algorithm for supervised classification, see Lee, Y. (2013), <doi:10.1214/13-EJS810> and Lee, E-K. (2018) <doi:10.18637/jss.v083.i08>. The algorithm is changed in two ways: improving prediction boundaries by modifying the choice of split points-through class subsetting; and increasing flexibility by allowing multiple splits per group.

This package provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).

ProTracker is a popular music tracker to sequence music on a Commodore Amiga machine. This package offers the opportunity to import, export, manipulate and play ProTracker module files. Even though the file format could be considered archaic, it still remains popular to this date. This package intends to contribute to this popularity and therewith keeping the legacy of ProTracker and the Commodore Amiga alive. This package is the successor of ProTrackR providing better performance.

This package provides an R interface to the PCATS API <https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit tasks and retrieve results.

Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.

An easy-to-use tool for implementing Neural Ordinary Differential Equations (NODEs) in pharmacometric software such as Monolix', NONMEM', and nlmixr2', see Bräm et al. (2024) <doi:10.1007/s10928-023-09886-4> and Bräm et al. (2025) <doi:10.1002/psp4.13265>. The main functionality is to automatically generate structural model code describing computations within a neural network. Additionally, parameters and software settings can be initialized automatically. For using these additional functionalities with Monolix', pmxNODE interfaces with MonolixSuite via the lixoftConnectors package. The lixoftConnectors package is distributed with MonolixSuite (<https://monolixsuite.slp-software.com/r-functions/2024R1/package-lixoftconnectors>) and is not available from public repositories.

Utilize the Bayesian prior and posterior predictive checking approach to provide a statistical assessment of replication success and failure. The package is based on the methods proposed in Zhao,Y., Wen X.(2021) <arXiv:2105.03993>.