Supports propensity score-based methodsâ including matching, stratification, and weightingâ for estimating causal treatment effects. It also implements calibration using negative control outcomes to enhance robustness. debiasedTrialEmulation facilitates effect estimation for both binary and time-to-event outcomes, supporting risk ratio (RR), odds ratio (OR), and hazard ratio (HR) as effect measures. It integrates statistical modeling and visualization tools to assess covariate balance, equipoise, and bias calibration. Additional methodsâ including approaches to address immortal time bias, information bias, selection bias, and informative censoringâ are under development. Users interested in these extended features are encouraged to contact the package authors.

With bivariate data, it is possible to calculate 2-dimensional kernel density estimates that return polygons at given levels of probability. densityarea returns these polygons for analysis, including for calculating their area.

This package provides a non-drawing graphic device for benchmarking purpose. In order to properly benchmark graphic drawing code it is necessary to factor out the device implementation itself so that results are not related to the specific graphics device used during benchmarking. The devoid package implements a graphic device that accepts all the required calls from R's graphic engine but performs no action. Apart from benchmarking it is unlikely that this device has any practical use.

This package provides tools to identify, quantify, analyze, and visualize growth suppression events in tree rings that are often produced by insect defoliation. Described in Guiterman et al. (2020) <doi:10.1016/j.dendro.2020.125750>.

Non-iterative estimator for the cumulative distribution of a doubly truncated variable. de Uña-à lvarez J. (2018) <doi:10.1007/978-3-319-73848-2_37>.

An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.

Managing postgraduate programmes involves extracting information from Lattes CVs. This information can be used for strategic planning and self-evaluation, as well as for producing reports on the Sucupira Platform. Summary reports are produced for each period and course (specialisation, master's and doctorate), showing bibliographic production with and without student participation, as well as papers at events, technical or technological production, ongoing and completed supervision, research projects, exchanges (visiting professor, postdoctoral or short-term leave), awards and general activity indicators. Based on this information, a detailed report is then drawn up for each lecturer, taking into account their participation in exam boards, their research project contributions, their technical collaborations (e.g. advisory committee, editorial board) and the subjects they teach. For more details see Pagliosa and Nascimento (2021) <https://repositorio.ufsc.br/bitstream/handle/123456789/231602/ManualLattesGeociencias11_2021_versaobeta%20%281%29.pdf?sequence=1&isAllowed=y>.

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.

This package provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at <https://osf.io/9jfqr/>, published version: <doi:10.1037/rev0000411>). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, <doi:10.1037/a0019737>), and various race models. C++ code for dynWEV and 2DSD is based on the rtdists package by Henrik Singmann.

Set of tools aimed at processing meteorological data, converting hourly recorded data to daily, monthly and annual data.

This package provides models to fit the dynamics of a regulated system experiencing exogenous inputs. The underlying models use differential equations and linear mixed-effects regressions to estimate the coefficients of the equation. With them, the functions can provide an estimated signal. The package provides simulation and analysis functions and also print, summary, plot and predict methods, adapted to the function outputs, for easy implementation and presentation of results.

The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that best describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a dark list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed.

Add a "Did You Mean" feature to the R interactive. With this package, error messages for misspelled input of variable names or package names suggest what you really want to do in addition to notification of the mistake.

This package provides functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

Allows users to quickly and easily describe data using common descriptive statistics.

Move elements between containers in Shiny without explicitly using JavaScript'. It can be used to build custom inputs or to change the positions of user interface elements like plots or tables.

Phone numbers are often represented as strings because there is no obvious and suitable native representation for them. This leads to high memory use and a lack of standard representation. The package provides integer representation of Australian phone numbers with optional raw vector calling code. The package name is an extension of au and ph'.

Implementation of DetMCD, a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, Rousseeuw and Verdonck (2012) <doi:10.1080/10618600.2012.672100>.

Estimators of Difference-in-Differences based on de Chaisemartin and D'Haultfoeuille.

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

This package provides tools for estimating the Remaining Useful Life (RUL) of degrading systems using linear mixed-effects models and creating a health index. It supports both univariate and multivariate degradation signals. For multivariate inputs, the signals are merged into a univariate health index prior to modeling. Linear and exponential degradation trajectories are supported (the latter using a log transformation). Remaining Useful Life (RUL) distributions are estimated using Bayesian updating for new units, enabling on-site predictive maintenance. Based on the methodology of Liu and Huang (2016) <doi:10.1109/TASE.2014.2349733>.

An R interface to the codediff JavaScript library (a copy of which is included in the package, see <https://github.com/danvk/codediff.js> for information). Allows for visualization of the difference between 2 files, usually text files or R scripts, in a browser.

This package implements the de-biased estimator for low-rank matrix completion and provides confidence intervals for entries of interest. See: by Chen et al. (2019) <doi:10.1073/pnas.1910053116>, Mai (2021) <arXiv:2103.11749>.

Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.