Implementation of a KL-based (Kullback-Leibler) test for MCAR (Missing Completely At Random) in the context of missing data as introduced in Michel et al. (2021) <arXiv:2109.10150>.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

Miscellaneous printing of numeric or statistical results in R Markdown or Quarto documents according to guidelines of the "Publication Manual" of the American Psychological Association (2020, ISBN: 978-1-4338-3215-4). These guidelines are usually referred to as APA style (<https://apastyle.apa.org/>) and include specific rules on the formatting of numbers and statistical test results. APA style has to be implemented when submitting scientific reports in a wide range of research fields, especially in the social sciences. The default output of numbers in the R console or R Markdown and Quarto documents does not meet the APA style requirements, and reformatting results manually can be cumbersome and error-prone. This package covers the automatic conversion of R objects to textual representations that meet the APA style requirements, which can be included in R Markdown or Quarto documents. It covers some basic statistical tests (t-test, ANOVA, correlation, chi-squared test, Wilcoxon test) as well as some basic number printing manipulations (formatting p-values, removing leading zeros for numbers that cannot be greater than one, and others). Other packages exist for formatting numbers and tests according to the APA style guidelines, such as papaja (<https://cran.r-project.org/package=papaja>) and apa (<https://cran.r-project.org/package=apa>), but they do not offer all convenience functionality included in prmisc'. The vignette has an overview of most of the functions included in the package.

This package provides functions for data normalization and transformation in preprocessing stages. Implements scaling methods (min-max, Z-score, L2 normalization) and power transformations (Box-Cox, Yeo-Johnson). Box-Cox transformation is described in Box and Cox (1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Yeo-Johnson transformation in Yeo and Johnson (2000) <doi:10.1093/biomet/87.4.954>.

General implementation of core function from phase-type theory. PhaseTypeR can be used to model continuous and discrete phase-type distributions, both univariate and multivariate. The package includes functions for outputting the mean and (co)variance of phase-type distributions; their density, probability and quantile functions; functions for random draws; functions for reward-transformation; and functions for plotting the distributions as networks. For more information on these functions please refer to Bladt and Nielsen (2017, ISBN: 978-1-4939-8377-3) and Campillo Navarro (2019) <https://orbit.dtu.dk/en/publications/order-statistics-and-multivariate-discrete-phase-type-distributio>.

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.

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.

This package provides a system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.

Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the Efftox (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.

In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.

This package provides a set of basic tools for generating, analyzing, summarizing and visualizing finite partially ordered sets. In particular, it implements flexible and very efficient algorithms for the extraction of linear extensions and for the computation of mutual ranking probabilities and other user-defined functionals, over them. The package is meant as a computationally efficient "engine", for the implementation of data analysis procedures, on systems of multidimensional ordinal indicators and partially ordered data, in the spirit of Fattore, M. (2016) "Partially ordered sets and the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-015-1059-6>, and Fattore M. and Arcagni, A. (2018) "A reduced posetic approach to the measurement of multidimensional ordinal deprivation", Social Indicators Research <DOI:10.1007/s11205-016-1501-4>.

This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST) person parameters for the 1,2,3,4-PL model and the GPCM (generalized partial credit model). The parameters are estimated under the assumption that the item parameters are known and fixed. The package is useful e.g. in the case that items from an item pool / item bank with known item parameters are administered to a new population of test-takers and an ability estimation for every test-taker is needed.

An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred founders. pedprobr is part of the pedsuite', a collection of packages for pedigree analysis in R. In particular, pedprobr depends on pedtools for pedigree manipulations and pedmut for mutation modelling. For more information, see Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).

This package provides adds postfix and infix logic operators for if, then, unless, and otherwise.

Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis API is used by organizations such as Statistics Sweden and Statistics Finland to disseminate data. The R package can interact with all PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract metadata and extract and parse statistics to R data.frame format. PX-Web is a solution to disseminate PC-Axis data files in dynamic tables on the web. Since 2013 PX-Web contains an API to disseminate PC-Axis files.

Global hypothesis tests combine information across multiple endpoints to test a single hypothesis. The prediction test is a recently proposed global hypothesis test with good performance for small sample sizes and many endpoints of interest. The test is also flexible in the types and combinations of expected results across the individual endpoints. This package provides functions for data processing and calculation of the prediction test.

Conduct simulation-based customized power calculation for clustered time to event data in a mixed crossed/nested design, where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power, motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2025) <doi:10.51387/25-NEJSDS76>. This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for within-subject (cell line) correlation while holding different distributional assumptions about the outcome. Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.

This package provides a tidyverse'-style interface to the Brazilian Central Bank (<https://www.bcb.gov.br>) PIX Open Data API <https://olinda.bcb.gov.br/olinda/servico/Pix_DadosAbertos/versao/v1/aplicacao#!/recursos>. Retrieve statistics on PIX keys, transactions by municipality, and monthly transaction summaries. All functions return tibbles and support OData query parameters for filtering, selecting, and ordering data.

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.

This package implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models.

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).

In the situation when multiple alternative treatments or interventions available, different population groups may respond differently to different treatments. This package implements a method that discovers the population subgroups in which a certain treatment has a better effect than the other alternative treatments. This is done by first estimating the treatment effect for a given treatment and its uncertainty by computing random forests, and the resulting model is summarized by a decision tree in which the probabilities that the given treatment is best for a given subgroup is shown in the corresponding terminal node of the tree.

Generates chronological and ordered p-plots for data vectors or vectors of p-values. The p-plot visualizes the evolution of the p-value of a significance test across the sampled data. It allows for assessing the consistency of the observed effects, for detecting the presence of potential moderator variables, and for estimating the influence of outlier values on the observed results. For non-significant findings, it can diagnose patterns indicative of underpowered study designs. The p-plot can thus either back the binary accept-vs-reject decision of common null-hypothesis significance tests, or it can qualify this decision and stimulate additional empirical work to arrive at more robust and replicable statistical inferences.