Change package name to flipr.

.Rbuildignore

@@ -1,4 +1,4 @@
-^psi\.Rproj$
+^flipr\.Rproj$
 ^\.Rproj\.user$
 ^README\.Rmd$
 ^vignettes/articles$

DESCRIPTION

@@ -1,5 +1,5 @@
-Package: psi
-Title: Permutations for Statistical Inference
+Package: flipr
+Title: Flexible Inference via Permutations in R
 Version: 0.1.0
 Authors@R: c(
     person(given = "Alessia", 
@@ -16,7 +16,7 @@ Authors@R: c(
            role = "aut", 
            email = "[email protected]"))
 Description: A flexible permutation framework for hypothesis 
-  testing, ANOVA and regression anlaysis of complex data.
+  testing, ANOVA and regression analysis of complex data.
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
@@ -34,3 +34,5 @@ Suggests:
     plotly,
     widgetframe
 VignetteBuilder: knitr
+URL: https://astamm.github.io/flipr/, https://github.com/astamm/flipr/
+BugReports: https://github.com/astamm/flipr/issues/

NEWS.md

@@ -1,4 +1,4 @@
-# psi 0.1.0
+# flipr 0.1.0
 
 * Initial release.
 * Added a `NEWS.md` file to track changes to the package.

R/two-sample-test.R

@@ -23,7 +23,7 @@
 #'   These can be \code{\link{stat_hotelling}} or user-specified functions that
 #'   define desired statistics. See the section *User-supplied statistic
 #'   function* for more information on how these user-supplied functions should
-#'   be structured for compatibility with the PSI framwork. Default is
+#'   be structured for compatibility with the **flipr** framwork. Default is
 #'   \code{\link{stat_hotelling}}.
 #' @param B The number of sampled permutation. Default is `1000L`.
 #' @param test A string specifying if performing an exact test through the use

README.Rmd

@@ -13,29 +13,34 @@ knitr::opts_chunk$set(
 )
 ```
 
-# Overview <a href='https://astamm.github.io/psi'><img src='man/figures/logo.png' align="right" height="139" /></a>
+# Overview <a href='https://astamm.github.io/flipr/'><img src='man/figures/logo.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
-[![check-standard](https://github.com/astamm/psi/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/psi/actions)
-[![test-coverage](https://github.com/astamm/psi/workflows/test-coverage/badge.svg)](https://github.com/astamm/psi/actions)
-[![Codecov test coverage](https://codecov.io/gh/astamm/psi/branch/master/graph/badge.svg)](https://codecov.io/gh/astamm/psi?branch=master)
-[![pkgdown](https://github.com/astamm/psi/workflows/pkgdown/badge.svg)](https://github.com/astamm/psi/actions)
-[![CRAN status](https://www.r-pkg.org/badges/version/psi)](https://CRAN.R-project.org/package=psi)
+[![check-standard](https://github.com/astamm/flipr/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/flipr/actions)
+[![test-coverage](https://github.com/astamm/flipr/workflows/test-coverage/badge.svg)](https://github.com/astamm/flipr/actions)
+[![Codecov test coverage](https://codecov.io/gh/astamm/flipr/branch/master/graph/badge.svg)](https://codecov.io/gh/astamm/flipr?branch=master)
+[![pkgdown](https://github.com/astamm/flipr/workflows/pkgdown/badge.svg)](https://github.com/astamm/flipr/actions)
+[![CRAN status](https://www.r-pkg.org/badges/version/flipr)](https://CRAN.R-project.org/package=flipr)
 <!-- badges: end -->
 
-The goal of the **psi** package is to provide a flexible framework for making inference via permutation. The idea is to promote the permutation framework as an incredibly well-suited tool for hypothesis testing on complex data. You supply your data, as complex as it might be, in the form of lists in which each entry stores one data point in a representation that suits you and **psi** takes care of the permutation magic and provides you with the result of the permutation test. Permutation tests are especially appealing because they are exact no matter how small or big your sample sizes are. You can also use the so-called *non-parametric combination* approach in this setting to combine several statistics to better target the alternative hypothesis you are testing against. Asymptotic consistency is also guaranteed under mild conditions on the statistic you use. Currently, you can do two-sample tests. We plan on adding very soon one-sample tests, ANOVA and regression to the list as well.
+The goal of the **flipr** package is to provide a flexible framework for making inference via permutation. The idea is to promote the permutation framework as an incredibly well-suited tool for hypothesis testing on complex data. You supply your data, as complex as it might be, in the form of lists in which each entry stores one data point in a representation that suits you and **flipr** takes care of the permutation magic and provides you with the result of the permutation test. Permutation tests are especially appealing because they are exact no matter how small or big your sample sizes are. You can also use the so-called *non-parametric combination* approach in this setting to combine several statistics to better target the alternative hypothesis you are testing against. Asymptotic consistency is also guaranteed under mild conditions on the statistic you use. Currently, you can do two-sample tests. We plan on adding very soon one-sample tests, ANOVA and regression to the list as well.
 
 ## Installation
 
-You can install the development version from [GitHub](https://github.com/) with:
+You can install the latest stable version of **flipr** on CRAN with:
+``` r
+install.packages("flipr")
+```
+
+Or you can install the development version from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("remotes")
-remotes::install_github("astamm/psi")
+remotes::install_github("astamm/flipr")
 ```
 ## Example
 
-We hereby use the very simple t-test for comparing the means of two univariate samples to show how easy it is to carry out a permutation test with **psi**.
+We hereby use the very simple t-test for comparing the means of two univariate samples to show how easy it is to carry out a permutation test with **flipr**.
 
 Let us first generate a first sample of size 10 governed by a Gaussian distribution of mean 0 and unit variance:
 ```{r sample1}
@@ -49,8 +54,8 @@ set.seed(1234)
 x2 <- rnorm(n = 10, mean = 3, sd = 1)
 ```
 
-We can implement the squared $t$-statistic as a function that plays well with **psi** as follows:
-```{r tstat-psi}
+We can implement the squared $t$-statistic as a function that plays well with **flipr** as follows:
+```{r tstat-flipr}
 stat_t2 <- function(data, indices) {
   n <- length(data)
   n1 <- length(indices)
@@ -64,9 +69,9 @@ stat_t2 <- function(data, indices) {
 
 Note that the square is needed as permutation tests look for large values of the test statistic to find evidence against the null hypothesis.
 
-Now we can simply use the function `psi::two_sample_test()` to get the result of the test:
-```{r ttest-psi}
-test_t2 <- psi::two_sample_test(
+Now we can simply use the function `flipr::two_sample_test()` to get the result of the test:
+```{r ttest-flipr}
+test_t2 <- flipr::two_sample_test(
   x = x1, 
   y = x2, 
   statistic = stat_t2, 

README.md

@@ -1,28 +1,28 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# Overview <a href='https://astamm.github.io/psi'><img src='man/figures/logo.png' align="right" height="139" /></a>
+# Overview <a href='https://astamm.github.io/flipr/'><img src='man/figures/logo.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
 
-[![check-standard](https://github.com/astamm/psi/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/psi/actions)
-[![test-coverage](https://github.com/astamm/psi/workflows/test-coverage/badge.svg)](https://github.com/astamm/psi/actions)
+[![check-standard](https://github.com/astamm/flipr/workflows/R-CMD-check/badge.svg)](https://github.com/astamm/flipr/actions)
+[![test-coverage](https://github.com/astamm/flipr/workflows/test-coverage/badge.svg)](https://github.com/astamm/flipr/actions)
 [![Codecov test
-coverage](https://codecov.io/gh/astamm/psi/branch/master/graph/badge.svg)](https://codecov.io/gh/astamm/psi?branch=master)
-[![pkgdown](https://github.com/astamm/psi/workflows/pkgdown/badge.svg)](https://github.com/astamm/psi/actions)
+coverage](https://codecov.io/gh/astamm/flipr/branch/master/graph/badge.svg)](https://codecov.io/gh/astamm/flipr?branch=master)
+[![pkgdown](https://github.com/astamm/flipr/workflows/pkgdown/badge.svg)](https://github.com/astamm/flipr/actions)
 [![CRAN
-status](https://www.r-pkg.org/badges/version/psi)](https://CRAN.R-project.org/package=psi)
+status](https://www.r-pkg.org/badges/version/flipr)](https://CRAN.R-project.org/package=flipr)
 <!-- badges: end -->
 
-The goal of the **psi** package is to provide a flexible framework for
+The goal of the **flipr** package is to provide a flexible framework for
 making inference via permutation. The idea is to promote the permutation
 framework as an incredibly well-suited tool for hypothesis testing on
 complex data. You supply your data, as complex as it might be, in the
 form of lists in which each entry stores one data point in a
-representation that suits you and **psi** takes care of the permutation
-magic and provides you with the result of the permutation test.
-Permutation tests are especially appealing because they are exact no
-matter how small or big your sample sizes are. You can also use the
+representation that suits you and **flipr** takes care of the
+permutation magic and provides you with the result of the permutation
+test. Permutation tests are especially appealing because they are exact
+no matter how small or big your sample sizes are. You can also use the
 so-called *non-parametric combination* approach in this setting to
 combine several statistics to better target the alternative hypothesis
 you are testing against. Asymptotic consistency is also guaranteed under
@@ -32,19 +32,25 @@ and regression to the list as well.
 
 ## Installation
 
-You can install the development version from
+You can install the latest stable version of **flipr** on CRAN with:
+
+``` r
+install.packages("flipr")
+```
+
+Or you can install the development version from
 [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("remotes")
-remotes::install_github("astamm/psi")
+remotes::install_github("astamm/flipr")
 ```
 
 ## Example
 
 We hereby use the very simple t-test for comparing the means of two
 univariate samples to show how easy it is to carry out a permutation
-test with **psi**.
+test with **flipr**.
 
 Let us first generate a first sample of size 10 governed by a Gaussian
 distribution of mean 0 and unit variance:
@@ -63,7 +69,7 @@ x2 <- rnorm(n = 10, mean = 3, sd = 1)
 ```
 
 We can implement the squared *t*-statistic as a function that plays well
-with **psi** as follows:
+with **flipr** as follows:
 
 ``` r
 stat_t2 <- function(data, indices) {
@@ -81,11 +87,11 @@ Note that the square is needed as permutation tests look for large
 values of the test statistic to find evidence against the null
 hypothesis.
 
-Now we can simply use the function `psi::two_sample_test()` to get the
+Now we can simply use the function `flipr::two_sample_test()` to get the
 result of the test:
 
 ``` r
-test_t2 <- psi::two_sample_test(
+test_t2 <- flipr::two_sample_test(
   x = x1, 
   y = x2, 
   statistic = stat_t2, 

vignettes/alternative.Rmd

@@ -15,8 +15,8 @@ knitr::opts_chunk$set(
 )
 ```
 
-```{r setup}
-library(psi)
+```{r setup, include=FALSE}
+library(flipr)
 ```
 
 In this article, we discuss a key difference between the traditional
@@ -98,7 +98,7 @@ same scale even though they might look at very different features of the
 distributions. They can therefore be combined in various ways to provide
 a single test statistic value to be used in the testing procedure. There
 are several possible **combining functions** to do this fusion of
-p-values. The package **psi** currently implements:
+p-values. The package **flipr** currently implements:
 
 -   Tippett's combining function:
     $\mathrm{Tippett}(p_1, …, p_L) = 1 - \min_{\ell \in 1, \dots, L} p_\ell$;

vignettes/exactness.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
   comment = "#>"
 )
 library(tidyverse)
-library(psi)
+library(flipr)
 ```
 
 In this article, we discuss the exactness property of permutation tests,
@@ -202,7 +202,7 @@ $$ p_e \approx \frac{b+1}{m+1} - \int_0^{\frac{0.5}{m_t+1}} F_B (b; m, p_t) dp_t
 
 ## Comparison by empirical evidence
 
-In **psi**, you perform a permutation test using the estimator
+In **flipr**, you perform a permutation test using the estimator
 $\widehat{p_\infty}$ of the p-value $p_\infty$ by setting
 `test == "approximate"`. This provides a non-exact test but an unbiased
 estimate of $p_\infty$. You perform a permutation test using the