This package provides functions which can be used to support the Multicriteria Decision Analysis (MCDA) process involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).

This package provides functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.

This package provides a collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Collection of functions for working with multi-well microtitre plates, mainly 96, 384 and 1536 well plates.

The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.

Exports an enhanced version of the tools::parseLatex() function to handle LaTeX syntax more accurately. Also includes numerous functions for searching and modifying LaTeX source.

Handles and formats author information in scientific writing in R Markdown and Quarto'. plume provides easy-to-use and flexible tools for inserting author data in YAML as well as generating author and contribution lists (among others) as strings from tabular data.

This package implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.

This package implements estimation and testing procedures for evaluating an intermediate biomarker response as a principal surrogate of a clinical response to treatment (i.e., principal stratification effect modification analysis), as described in Juraska M, Huang Y, and Gilbert PB (2020), Inference on treatment effect modification by biomarker response in a three-phase sampling design, Biostatistics, 21(3): 545-560 <doi:10.1093/biostatistics/kxy074>. The methods avoid the restrictive placebo structural risk modeling assumption common to past methods and further improve robustness by the use of nonparametric kernel smoothing for biomarker density estimation. A randomized controlled two-group clinical efficacy trial is assumed with an ordered categorical or continuous univariate biomarker response measured at a fixed timepoint post-randomization and with a univariate baseline surrogate measure allowed to be observed in only a subset of trial participants with an observed biomarker response (see the flexible three-phase sampling design in the paper for details). Bootstrap-based procedures are available for pointwise and simultaneous confidence intervals and testing of four relevant hypotheses. Summary and plotting functions are provided for estimation results.

This package provides a collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.

Data and statistics of Pakistan Social and Living Standards Measurement (PSLM) survey 2014-15 from Pakistan Bureau of Statistics (<http://www.pbs.gov.pk/>).

The plotcli package provides terminal-based plotting in R. It supports colored scatter plots, line plots, bar plots, boxplots, histograms, density plots, and more. The ggplotcli() function is a universal converter that renders any ggplot2 plot in the terminal using Unicode Braille characters or ASCII. Features include support for 15+ geom types, faceting (facet_wrap/facet_grid), automatic theme detection, legends, optimized color mapping, and multiple canvas types.

Design and analyze two-stage randomized trials with a continuous outcome measure. The package contains functions to compute the required sample size needed to detect a given preference, treatment, and selection effect; alternatively, the package contains functions that can report the study power given a fixed sample size. Finally, analysis functions are provided to test each effect using either summary data (i.e. means, variances) or raw study data <doi:10.18637/jss.v094.c02>.

This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.

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.

For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.

Includes a collection of functions presented in "Measuring stability in ecological systems without static equilibria" by Clark et al. (2022) <doi:10.1002/ecs2.4328> in Ecosphere. These can be used to estimate the parameters of a stochastic state space model (i.e. a model where a time series is observed with error). The goal of this package is to estimate the variability around a deterministic process, both in terms of observation error - i.e. variability due to imperfect observations that does not influence system state - and in terms of process noise - i.e. stochastic variation in the actual state of the process. Unlike classical methods for estimating variability, this package does not necessarily assume that the deterministic state is fixed (i.e. a fixed-point equilibrium), meaning that variability around a dynamic trajectory can be estimated (e.g. stochastic fluctuations during predator-prey dynamics).

Supports propensity score weighting analysis of observational studies and randomized trials. Enables the estimation and inference of average causal effects with binary and multiple treatments using overlap weights (ATO), inverse probability of treatment weights (ATE), average treatment effect among the treated weights (ATT), matching weights (ATM) and entropy weights (ATEN), with and without propensity score trimming. These weights are members of the family of balancing weights introduced in Li, Morgan and Zaslavsky (2018) <doi:10.1080/01621459.2016.1260466> and Li and Li (2019) <doi:10.1214/19-AOAS1282>.

The goal of planets is to provide of very simple and accessible data containing basic information from all known planets.

Applies phylogenetic comparative methods (PCM) and phylogenetic trait imputation using structural equation models (SEM), extending methods from Thorson et al. (2023) <doi:10.1111/2041-210X.14076>. This implementation includes a minimal set of features, to allow users to easily read all of the documentation and source code. PCM using SEM includes phylogenetic linear models and structural equation models as nested submodels, but also allows imputation of missing values. Features and comparison with other packages are described in Thorson and van der Bijl (2023) <doi:10.1111/jeb.14234>.

This package provides a framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

This package provides an interface to PDFMiner <https://github.com/pdfminer/pdfminer.six> a Python package for extracting information from PDF'-files. PDFMiner has the goal to get all information available in a PDF'-file, position of the characters, font type, font size and informations about lines. Which makes it the perfect starting point for extracting tables from PDF'-files. More information can be found in the package README'-file.

NOTE: PARAMLINK HAS BEEN SUPERSEDED BY THE PEDSUITE PACKAGES (<https://magnusdv.github.io/pedsuite/>). PARAMLINK IS MAINTAINED ONLY FOR LEGACY PURPOSES AND SHOULD NOT BE USED IN NEW PROJECTS. A suite of tools for analysing pedigrees with marker data, including parametric linkage analysis, forensic computations, relatedness analysis and marker simulations. The core of the package is an implementation of the Elston-Stewart algorithm for pedigree likelihoods, extended to allow mutations as well as complex inbreeding. Features for linkage analysis include singlepoint LOD scores, power analysis, and multipoint analysis (the latter through a wrapper to the MERLIN software). Forensic applications include exclusion probabilities, genotype distributions and conditional simulations. Data from the Familias software can be imported and analysed in paramlink'. Finally, paramlink offers many utility functions for creating, manipulating and plotting pedigrees with or without marker data (the actual plotting is done by the kinship2 package).

Package for corpus analysis using the Corpus Workbench ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing and querying large corpora. The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora.