Differential Item Functioning (DIF) Analysis with shiny application interfaces. You can run the functions in this package without any arguments and perform your DIF analysis using user-friendly interfaces.

Manage your source code dependencies by decorating your existing R code with special, roxygen'-style comments.

This package provides a datetime range picker widget for usage in Shiny'. It creates a calendar allowing to select a start date and an end date as well as two fields allowing to select a start time and an end time.

Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.

An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants. denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size. Observed variant frequencies can then be compared against expectation in a Poisson framework. denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.

This package provides tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

This package provides functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.

This package provides a toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.

An R DataBase Interface ('DBI') compatible interface to various database platforms ('PostgreSQL', Oracle', Microsoft SQL Server', Amazon Redshift', Microsoft Parallel Database Warehouse', IBM Netezza', Apache Impala', Google BigQuery', Snowflake', Spark', SQLite', and InterSystems IRIS'). Also includes support for fetching data as Andromeda objects. Uses either Java Database Connectivity ('JDBC') or other DBI drivers to connect to databases.

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

This package provides tools for working with a new versatile discrete distribution, the db ("discretised Beta") distribution. This package provides density (probability), distribution, inverse distribution (quantile) and random data generation functions for the db family. It provides functions to effect conveniently maximum likelihood estimation of parameters, and a variety of useful plotting functions. It provides goodness of fit tests and functions to calculate the Fisher information, different estimates of the hessian of the log likelihood and Monte Carlo estimation of the covariance matrix of the maximum likelihood parameter estimates. In addition it provides analogous tools for working with the beta-binomial distribution which has been proposed as a competitor to the db distribution.

The goal of dynamicpv is to provide a simple way to calculate (net) present values and outputs from health economic models (especially cost-effectiveness and budget impact) in discrete time that reflect dynamic pricing and dynamic uptake. Dynamic pricing is also known as life cycle pricing; dynamic uptake is also known as multiple or stacked cohorts, or dynamic disease prevalence. Shafrin (2024) <doi:10.1515/fhep-2024-0014> provides an explanation of dynamic value elements, in the context of Generalized Cost Effectiveness Analysis, and Puls (2024) <doi:10.1016/j.jval.2024.03.006> reviews challenges of incorporating such dynamic value elements. This package aims to reduce those challenges.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the Armadillo C++ library and the collapse package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the news content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

This package implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

Download and import time series from <http://www.dataseries.org>, a comprehensive and up-to-date collection of open data from Switzerland.

Work within the dplyr workflow to add random variates to your data frame. Variates can be added at any level of an existing column. Also, bounds can be specified for simulated variates.

Estimates the conditional association between an exposure and an outcome given covariates. Three methods are implemented: O-estimation, where a nuisance model for the association between the covariates and the outcome is used; E-estimation where a nuisance model for the association between the covariates and the exposure is used, and doubly robust (DR) estimation where both nuisance models are used. In DR-estimation, the estimates will be consistent when at least one of the nuisance models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.

An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.

This package implements Meng's data defect index (ddi), which represents the degree of sample bias relative to an iid sample. The data defect correlation (ddc) represents the correlation between the outcome of interest and the selection into the sample; when the sample selection is independent across the population, the ddc is zero. Details are in Meng (2018) <doi:10.1214/18-AOAS1161SF>, "Statistical Paradises and Paradoxes in Big Data (I): Law of Large Populations, Big Data Paradox, and the 2016 US Presidential Election." Survey estimates from the Cooperative Congressional Election Study (CCES) is included to replicate the article's results.

Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the decompr and gvc packages have each country-industry combination occur along both edges of the matrix.

Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by OpenBUGS'. The method is described: Wang and Lee (2014) <doi:10.1016/j.csda.2013.06.011> and exercised in Lee (2009) <http://hdl.handle.net/1993/21352>.

Allows manual creation of themes and logos to be used in applications created using the shinydashboard package. Removes the need to change the underlying css code by wrapping it into a set of convenient R functions.

By adding over-relaxation factor to PXEM (Parameter Expanded Expectation Maximization) method, the MOPXEM (Monotonically Overrelaxed Parameter Expanded Expectation Maximization) method is obtained. Compare it with the existing EM (Expectation-Maximization)-like methods. Then, distribute and process five methods and compare them, achieving good performance in convergence speed and result quality.The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

You can retrieve Spotify API Information such as artists, albums, tracks, features tracks, recommendations or related artists. This package allows you to search all the information by name and also includes a distance based algorithm to find similar songs. More information: <https://developer.spotify.com/documentation/web-api/> .