Web Client/Wrapper to the 'Figma API'

An easy-to-use web client/wrapper for the 'Figma API' < https://www.figma.com/developers/api>. It allows you to bring all data from a 'Figma' file to your 'R' session. This includes the data of all objects that you have drawn in this file, and their respective canvas/page metadata.

Objective Bayesian Distribution Fitting

Fits common univariate distributions using registered objective Bayesian priors, including Jeffreys, reference, and maximal data information priors. Model-specific posterior propriety and moment conditions are checked before computation. Exact simulation, marginalization, slice sampling, and adaptive Metropolis algorithms are selected from posterior structure, with a common interface for summaries, diagnostics, prediction, and pointwise log-likelihood evaluation. The reference-prior framework follows Bernardo (1979) .

Authoring Books and Technical Documents with R Markdown

Output formats and utilities for authoring books and technical documents with R Markdown.

Plots, Summary Statistics and Tools for Arena Simulation Users

Reads Arena < https://www.arenasimulation.com/> CSV output files and generates nice tables and plots. The package contains a Shiny App that can be used to interactively visualize Arena's results.

Model and Analyse Interval Data

Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.

Validation of Arguments and Objects in User-Defined Functions

Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Doubly Robust Difference-in-Differences Estimators

Implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) . 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.

Computational Tools for Economics

Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, ) growth model and Mankiw, Romer and Weil (1992, ) growth model.

Infer Gene Probabilistic Boolean Networks from Single-Cell Data

Given a gene regulatory boolean network and a RNA-seq dataset, this package computes protein activity normalised enrichment scores using 'VIPER', and then produces a probabilistic network using the scores as probabilities for fixed node activation or deactivation, in addition to the original Boolean functions. For more information, refer to the preprint: Victori and Buffa (2022) .

Bayes Linear Estimators for Finite Population

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) < https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.