Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.

The propensity score is one of the most widely used tools in studying the causal effect of a treatment, intervention, or policy. Given that the propensity score is usually unknown, it has to be estimated, implying that the reliability of many treatment effect estimators depends on the correct specification of the (parametric) propensity score. This package implements the data-driven nonparametric diagnostic tools for detecting propensity score misspecification proposed by Sant'Anna and Song (2019) <doi:10.1016/j.jeconom.2019.02.002>.

The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. Along with accompanied monobin and monobinShiny packages, PDtoolkit provides functions which are suitable for different data transformation and modeling tasks such as: imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, heterogeneity, discriminatory and predictive power of the model.

This package provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes.

Data and analysis from an experiment with improving touch typing speed, using the tDCS PlatoWork headset produced by PlatoScience.

This package contains various tools for conveniently downloading and editing taxon-specific datasets from the Paleobiology Database <https://paleobiodb.org>, extracting information on abundance, temporal distribution of subtaxa and taxonomic diversity through deep time, and visualizing these data in relation to phylogeny and stratigraphy.

Extends the S3 generic function knit_print() in knitr to automatically print some objects using an appropriate format such as Markdown or LaTeX. For example, data frames are automatically printed as tables, and the help() pages can also be rendered in knitr documents.

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

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.

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.

R API for Pathling', a tool for querying and transforming electronic health record data that is represented using the Fast Healthcare Interoperability Resources (FHIR) standard - see <https://pathling.csiro.au/docs>.

It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.

Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.

The functions are designed to find the efficient mean-variance frontier or portfolio weights for static portfolio (called Markowitz portfolio) analysis in resource economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), this package deals with the quadratic minimization of the variance-covariances without shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: <https://github.com/ysd2004/portn>.

Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.

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 tools for Bayesian power analysis and assurance calculations using the statistical frameworks of brms and INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".

Computes the minimum sample size required for the external validation of an existing multivariable prediction model using the criteria proposed by Archer (2020) <doi:10.1002/sim.8766> and Riley (2021) <doi:10.1002/sim.9025>.

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

An add-on to the party package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the party package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the party implementation. The package is compatible with random forests fit using the party and the randomForest package. The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Probabilistic factor analysis for spatially-aware dimension reduction across multi-section spatial transcriptomics data with millions of spatial locations. More details can be referred to Wei Liu, et al. (2023) <doi:10.1101/2023.07.11.548486>.

This package provides a fast and flexible framework for agglomerative partitioning. partition uses an approach called Direct-Measure-Reduce to create new variables that maintain the user-specified minimum level of information. Each reduced variable is also interpretable: the original variables map to one and only one variable in the reduced data set. partition is flexible, as well: how variables are selected to reduce, how information loss is measured, and the way data is reduced can all be customized. partition is based on the Partition framework discussed in Millstein et al. (2020) <doi:10.1093/bioinformatics/btz661>.

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 quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.