Collection of functions to get files in parquet format. Parquet is a columnar storage file format <https://parquet.apache.org/>. The files to convert can be of several formats ("csv", "RData", "rds", "RSQLite", "json", "ndjson", "SAS", "SPSS"...).

We consider the network structure detection for variables Y with auxiliary variables X accommodated, which are possibly subject to measurement error. The following three functions are designed to address various structures by different methods : one is NP_Graph() that is used for handling the nonlinear relationship between the responses and the covariates, another is Joint_Gaussian() that is used for correction in linear regression models via the Gaussian maximum likelihood, and the other Cond_Gaussian() is for linear regression models via conditional likelihood function.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

The PBIB designs are important type of incomplete block designs having wide area of their applications for example in agricultural experiments, in plant breeding, in sample surveys etc. This package constructs various series of PBIB designs and assists in checking all the necessary conditions of PBIB designs and the association scheme on which these designs are based on. It also assists in calculating the efficiencies of PBIB designs with any number of associate classes. The package also constructs Youden-m square designs which are Row-Column designs for the two-way elimination of heterogeneity. The incomplete columns of these Youden-m square designs constitute PBIB designs. With the present functionality, the package will be of immense importance for the researchers as it will help them to construct PBIB designs, to check if their PBIB designs and association scheme satisfy various necessary conditions for the existence, to calculate the efficiencies of PBIB designs based on any association scheme and to construct Youden-m square designs for the two-way elimination of heterogeneity. R. C. Bose and K. R. Nair (1939) <http://www.jstor.org/stable/40383923>.

The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.

Perform tasks commonly encountered when preparing and analysing demographic data. Some functions are intended for end users, and others for developers. Includes functions for working with life tables.

Analysis of features by phi delta diagrams. In particular, functions for reading data and calculating phi and delta as well as the functionality to plot it. Moreover it is possible to do further analysis on the data by generating rankings. For more information on phi delta diagrams, see also Giuliano Armano (2015) <doi:10.1016/j.ins.2015.07.028>.

XKCD described a supposedly "bad" colormap that it called a "Painbow" (see <https://xkcd.com/2537/>). But simple tests demonstrate that under some circumstances, the colormap can perform very well, and people can find information that is difficult to detect with the ggplot2 default and even supposedly "good" colormaps like viridis. This library let's you use the Painbow in your own ggplot graphs.

This package provides a comprehensive framework for model fitting and simulation of drug release kinetics, pharmacokinetics (PK), and pharmacodynamics (PD). The package implements widely used mechanistic and empirical models for in vitro drug release, including zero-order, first-order, Higuchi, Korsmeyer-Peppas, Hixson-Crowell, and Weibull models. Pharmacokinetic functionality includes linear and nonlinear functions for one- and two-compartment models for intravenous bolus and oral administration, Michaelis-Menten kinetics, and non-compartmental analysis (NCA). Pharmacodynamic and dose-response modeling is supported through Emax-based models, including stimulatory (sigmoid Emax) and inhibitory (sigmoid Imax) Hill models, four- and five-parameter logistic models, as well as median toxic dose (TD50) and lethal dose (LD50) models. The package is intended to support parameter estimation, simulation, and model comparison in pharmaceutical research, drug development, and pharmacometrics education. For more details, see Gabrielsson & Weiner (2000) <ISBN:9186274929>, Holford & Sheiner (1981) <doi:10.2165/00003088-198106060-00002>, and Manlapaz (2025) <doi:10.32614/CRAN.package.adsoRptionCMF>.

For a given graph containing vertices, edges, and a signal associated with the vertices, the PathwaySpace package performs a convolution operation, which involves a weighted combination of neighboring vertices and their associated signals. The package uses a decay function to project these signals, creating geodesic paths on a 2D-image space. PathwaySpace has various applications, such as visualizing network data in a graphical format that highlights the relationships and signal strengths between vertices. By combining graph theory, signal processing, and visualization, PathwaySpace provides a way of representing graph data on a continuous projection space. Based on methods introduced in Tercan et al. (2025) <doi:10.1016/j.xpro.2025.103681> and Ellrott et al. (2025) <doi:10.1016/j.ccell.2024.12.002>.

This package provides function for performing Bayesian survival regression using Horseshoe prior in the accelerated failure time model with log normal assumption in order to achieve high dimensional pan-cancer variable selection as developed in Maity et. al. (2019) <doi:10.1111/biom.13132>.

This package provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of palaeoverse is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

This package performs Bayesian arm-based network meta-analysis for datasets with binary, continuous, and count outcomes (Zhang et al., 2014 <doi:10.1177/1740774513498322>; Lin et al., 2017 <doi:10.18637/jss.v080.i05>).

This package creates and manages a provenance graph corresponding to the provenance created by the rdtLite package, which collects provenance from R scripts. rdtLite is available on CRAN. The provenance format is an extension of the W3C PROV JSON format (<https://www.w3.org/Submission/2013/SUBM-prov-json-20130424/>). The extended JSON provenance format is described in <https://github.com/End-to-end-provenance/ExtendedProvJson>.

This package provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).

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>.

Generate all necessary R/Rmd/shell files for data processing after running GGIR (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of SDG monitoring, as the survey produces information on 32 global SDG 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.

This version of the permutational algorithm generates a dataset in which event and censoring times are conditional on an user-specified list of covariates, some or all of which are time-dependent.

Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like paleotree and APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, paleobuddy can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.

This package provides a Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the BoolNet package, which is licensed under the Artistic License 2.0.

This package provides a suite of diagnostic tools for univariate point processes. This includes tools for simulating and fitting both common and more complex temporal point processes. We also include functions to visualise these point processes and collect existing diagnostic tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and Wu et al. (2021) <doi:10.1002/9781119821588.ch7>, which can be used to assess the fit of a chosen point process model.

Person fit statistics based on Quality Control measures are provided for questionnaires and tests given a specified IRT model. Statistics based on Cumulative Sum (CUSUM) charts are provided. Options are given for banks with polytomous and dichotomous data.

Recent years have seen an increased interest in novel methods for analyzing quantitative data from experimental psychology. Currently, however, they lack an established and accessible software framework. Many existing implementations provide no guidelines, consisting of small code snippets, or sets of packages. In addition, the use of existing packages often requires advanced programming experience. PredPsych is a user-friendly toolbox based on machine learning predictive algorithms. It comprises of multiple functionalities for multivariate analyses of quantitative behavioral data based on machine learning models.