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NEWS.md

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# causact (development version)
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# causact 0.5.7
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# causact 0.5.8
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* patch release to support changes to object types in ggplot2.
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# causact 0.5.6
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* patch release to fix numpyro integration issues. The default installation workflow now uses `numpyro==0.16.1` to avoid `TypeError` when using a discrete data distribution, e.g. Bernoulli.

R/data.R

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#' \item{CPI2017}{The Corruption Perceptions Index score for 2017: A country/territory’s score indicates the perceived level of public sector corruption on a scale of 0-100, where 0 means that a country is perceived as highly corrupt and a 100 means that a country is perceived as very clean. }
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#' \item{HDI2017}{The human development index score for 2017: the Human Development Index (HDI) is a measure of achievement in the basic dimensions of human development across countries. It is an index made from a simple unweighted average of a nation’s longevity, education and income and is widely accepted in development discourse.}
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#' }
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#' @source <https://www.transparency.org/en/cpi/2017> CPI data available from https://www.transparency.org/en/cpi/2017. Accessed Feb 24, 2024. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
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#' @source CPI data available from https://www.transparency.org/en/cpi/2017. Accessed Feb 24, 2024. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
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#' @source <https://hdr.undp.org/data-center/human-development-index#/indicies/HDI> HDA data accessed on Oct 1, 2018.
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#' @source <https://data.worldbank.org/> Population data accessed on Oct 1, 2018.
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"corruptDF"

README.md

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<!-- README.md is generated from README.Rmd. Please edit that file -->
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<!-- badges: start -->
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[![R-CMD-check](https://github.com/flyaflya/causact/workflows/R-CMD-check/badge.svg)](https://github.com/flyaflya/causact/actions)
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<img src="man/figures/gretaPost-1.png" alt="Credible interval plots." width="70%" />
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<p class="caption">
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Credible interval plots.
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</p>
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man/corruptDF.Rd

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tests/testthat/test-dag_plot.R

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test_that("dag plot creates graph", {
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df = data.frame(x = c(1:5),y = c(6:10))
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plotGr = df %>% dagp_plot()
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expect_true(is_ggplot(p))
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expect_true(ggplot2::is_ggplot(p))
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testthat::expect_no_error(
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df %>% dagp_plot(densityPlot = TRUE)
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)

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