Package: LoTTA 0.1.1

Julia Kowalska

LoTTA: Bayesian Inference in Regression Discontinuity Designs

Implementation of the LoTTA (Local Trimmed Taylor Approximation) model described in "Bayesian Regression Discontinuity Design with Unknown Cutoff" by Kowalska, van de Wiel, van der Pas (2024) <doi:10.48550/arXiv.2406.11585>.

Authors:Julia Kowalska [aut, cre, cph]

LoTTA_0.1.1.tar.gz
LoTTA_0.1.1.tar.gz(r-4.7-any)LoTTA_0.1.1.tar.gz(r-4.6-any)
LoTTA_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
LoTTA/json (API)

# Install 'LoTTA' in R:

install.packages('LoTTA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

jags cpp

1.30 score 163 downloads 8 exports 82 dependencies

Last updated from:d10d129953. Checks:4 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	158
source / vignettes	OK	196
linux-release-x86_64	OK	142
wasm-release	OK	114

Exports:LoTTA_fuzzy_BIN LoTTA_fuzzy_CONT LoTTA_plot_effect LoTTA_plot_outcome LoTTA_plot_treatment LoTTA_sharp_BIN LoTTA_sharp_CONT LoTTA_treatment

Dependencies:abind backports bayestestR boot broom car carData cli coda colorspace corrplot cowplot cpp11 datawizard Deriv doBy dplyr farver forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable insight isoband labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv microbenchmark minqa modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig polynom purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang rstatix runjags S7 scales SparseM stringi stringr survival tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr zoo

Citation

Readme and manuals

Help Manual

Help page	Topics
LoTTA_fuzzy_BIN	LoTTA_fuzzy_BIN
LoTTA_fuzzy_CONT	LoTTA_fuzzy_CONT
LoTTA_plot_effect	LoTTA_plot_effect
Function that visualizes the impact of the cutoff location on the treatment effect estimate. It plots too figures. The bottom figure depicts the posterior density of the cutoff location. The top figure depicts the box plot of the treatment effect given the cutoff point. If the prior on the cutoff location was discrete each box corresponds to a distinct cutoff point. If the prior was continuous each box correspond to an interval of cutoff values (the number of intervals can be changed through nbins).	LoTTA_plot_effect_DIS
LoTTA_plot_outcome	LoTTA_plot_outcome
Function that plots the median (or another quantile) of the LoTTA posterior treatment probability function along with the quanatile-based credible interval. The function is plotted on top of the binned input data. To bin the data, the score data is divided into bins of fixed length, then the proportion of treated is calculated in each bin. The proportions are plotted against the average values of the score in the corresponding bins. The data is binned separately on each side of the cutoff, the cutoff is marked on the plot with a dotted line. In case of an unknown cutoff, the MAP estimate is used.	LoTTA_plot_treatment
LoTTA_sharp_BIN	LoTTA_sharp_BIN
LoTTA_sharp_CONT	LoTTA_sharp_CONT
LoTTA_treatment	LoTTA_treatment

Package: LoTTA 0.1.1

LoTTA: Bayesian Inference in Regression Discontinuity Designs

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)