0.0.4 updates

- Changed license to MIT (after doing a bit of reading up on this)
- Added placeholder hex sticker

Author: jhelvy

.Rbuildignore

@@ -6,6 +6,7 @@
 ^pkgdown$
 ^\.github$
 vignettes/children
+^README\.Rmd$
 ^LICENSE\.md$
 ^logitr\.Rproj$
 ^doc$

DESCRIPTION

@@ -9,7 +9,7 @@ Authors@R: c(
            email   = "john.helveston@gmail.com",
            comment = c(ORCID = "0000-0002-2657-9191")))
 Description: logitr estimates multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. The program includes an option to run a multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the nloptr function to minimize the negative log-likelihood function. The package has additional functions for computing and comparing WTP from both preference space and WTP space models and for simulating the expected shares of a set of alternatives using an estimated model.
-License: GPL-3
+License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)

LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2014-2020
+COPYRIGHT HOLDER: John Paul Helveston

LICENSE.md

@@ -1,17 +1,41 @@
-# logitr 0.0.3: New simulation functionality
+# logitr 0.0.4
+
+Weighted models, new dataset, new encoding features
+
+## Summary of larger updates:
+
+- Added support for estimating weighted regressions
+- Added and improved documentation for new datasets: `yogurt`, `cars_china`, `cars_us`
+- Exported the `dummyCode()` function for automatically creating dummy-coded variables in a data frame.
+- Added support for auto dummy-coding categorical variables prior to model estimation
+- Major overhaul of documentation using {pkgdown}
+
+## Summary of smaller updates:
+
+- Changed license to MIT (after doing a bit of reading up on this)
+- Fixed dimension-matching issue with user-provided draws for mixed logit models
+- Fixed bug in `modelInputs` where `obsID` was not a vector for tibble inputs
+- Added placeholder hex sticker
+
+
+# logitr 0.0.3
+
+New simulation functionality
 
 ## Summary of larger updates:
 
-* Added support for simulating shares for a set of alternatives given an estimated model: `simulateShares()`. This is similar to the `predict()` function in mlogit.
-* Removed support for using an estimated preference space model as an input in the `options()` function. I found this just far too confusing, and instead encourage users to supply a WTP space model with the computed WTP from a preference space model as starting parameters.
+- Added support for simulating shares for a set of alternatives given an estimated model: `simulateShares()`. This is similar to the `predict()` function in mlogit.
+- Removed support for using an estimated preference space model as an input in the `options()` function. I found this just far too confusing, and instead encourage users to supply a WTP space model with the computed WTP from a preference space model as starting parameters.
 
 ## Summary of smaller updates:
 
-* Updated the `summary()` and main `logitr()` functions to keep the basic information (run #, log-likelihood value, number of iterations, and output status) whenever `numMultistarts` > 1.  Previously this information was only kept if `keepAllRuns` was set to `TRUE`.
+- Updated the `summary()` and main `logitr()` functions to keep the basic information (run #, log-likelihood value, number of iterations, and output status) whenever `numMultistarts` > 1.  Previously this information was only kept if `keepAllRuns` was set to `TRUE`.
+
 
 
+# logitr 0.0.2
 
-# logitr 0.0.2: Updates to options and a few small bug fixes
+Updates to options and a few small bug fixes
 
 ## Summary of larger updates:
 
@@ -38,6 +62,8 @@ getting the right logLik value too.
 
 
 
-# logitr 0.0.1: Full reboot of logitr!
+# logitr 0.0.1
+
+Full reboot of logitr!
 
 Long overdue, I decided to give the logitr program a full overhaul. This is the first version that is compiled as a proper R package that can be directly installed from Github. This version is much more robust and flexible than the prior, clunky collection of R files that I had previously been using to estimate logit models.

NEWS.md

@@ -0,0 +1,80 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  warning = FALSE,
+  message = FALSE,
+  comment = "#>",
+  fig.path = "man/figures/",
+  fig.retina = 3
+)
+```
+
+# logitr <a href='https://jhelvy.github.io/logitr/'><img src='man/figures/logitr-hex.png' align="right" height="139" /></a>
+
+<!-- badges: start -->
+[![Lifecycle:
+experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
+[![CRAN
+status](https://www.r-pkg.org/badges/version/logitr)](https://CRAN.R-project.org/package=logitr)
+<!-- badges: end -->
+
+**logitr** estimates multinomial (MNL) and mixed logit (MXL) models in
+R. Models can be estimated using “Preference” space or
+“Willingness-to-pay” (WTP) space [utility
+parameterizations](https://jhelvy.github.io/logitr/articles/utility_models.html).
+The current version includes support for:
+
+- Homogeneous multinomial logit (MNL) models
+- Heterogeneous mixed logit (MXL) models (with normal and log-normal parameter distributions).
+- Preference space utility parameterization.
+- WTP space utility parameterization.
+- An optional multistart optimization that uses different random starting points in each iteration (useful for non-convex problems like MXL models or models with WTP space parameterizations).
+
+The package also has additional functions for:
+
+- Computing and comparing WTP from both preference space and WTP space models.
+- Simulating the expected shares of a set of alternatives using an estimated model.
+
+Note: MXL models assume uncorrelated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in [Kenneth Train’s](http://eml.berkeley.edu/~train/) book [*Discrete Choice Methods with Simulation, 2nd Edition (New York: Cambridge University Press, 2009)*](http://eml.berkeley.edu/books/choice2.html).
+
+View the [basic usage](https://jhelvy.github.io/logitr/articles/basic_usage.html) page for details on how to use **logitr** to estimate models.
+
+## Installation
+
+The current version is not yet on CRAN, but you can install it from
+Github using the **devtools** library:
+
+```{r, eval=FALSE}
+devtools::install_github("jhelvy/logitr")
+```
+
+Load the library with:
+```{r, eval=FALSE}
+library(logitr)
+```
+
+## Required Libraries
+
+**logitr** requires the [**nloptr**](https://cran.r-project.org/web/packages/nloptr/index.html) library. This is because `nloptr()` allows for both the objective and gradient functions to be computed in a single function. This speeds up computation time considerably because both the objective and gradient functions require many of the same calculations (e.g. computing probabilities).
+
+## Author, Version, and License Information
+
+- Author: *John Paul Helveston* [www.jhelvy.com](http://www.jhelvy.com/)
+- Date First Written: *Sunday, September 28, 2014*
+- Most Recent Update: `r format(Sys.Date(), format="%B %d %Y")`
+- License: [MIT](https://github.com/jhelvy/logitr/blob/master/LICENSE.md)
+- [Latest Release](https://github.com/jhelvy/logitr/releases/latest): 0.0.4
+
+# Citation Information
+
+If you use this package for in a publication, I would greatly appreciate it if you cited it. You can get the citation information by typing `citation("logitr")` into R:
+
+```{r}
+citation("logitr")
+```

README.Rmd

@@ -1,13 +1,14 @@
 
-# logitr
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# logitr <a href='https://jhelvy.github.io/logitr/'><img src='man/figures/logitr-hex.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
 
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/logitr)](https://CRAN.R-project.org/package=logitr)
-
 <!-- badges: end -->
 
 **logitr** estimates multinomial (MNL) and mixed logit (MXL) models in
@@ -47,7 +48,15 @@ for details on how to use **logitr** to estimate models.
 The current version is not yet on CRAN, but you can install it from
 Github using the **devtools** library:
 
-    devtools::install_github("jhelvy/logitr")
+``` r
+devtools::install_github("jhelvy/logitr")
+```
+
+Load the library with:
+
+``` r
+library(logitr)
+```
 
 ## Required Libraries
 
@@ -59,19 +68,38 @@ computation time considerably because both the objective and gradient
 functions require many of the same calculations (e.g. computing
 probabilities).
 
-# Author, Version, and License Information
+## Author, Version, and License Information
 
   - Author: *John Paul Helveston*
     [www.jhelvy.com](http://www.jhelvy.com/)
   - Date First Written: *Sunday, September 28, 2014*
-  - Most Recent Update: *Thursday, Oct 22, 2020*
-  - License: GPL-3
-  - Latest Version: 1.2.0
+  - Most Recent Update: December 04 2020
+  - License:
+    [MIT](https://github.com/jhelvy/logitr/blob/master/LICENSE.md)
+  - [Latest Release](https://github.com/jhelvy/logitr/releases/latest):
+    0.0.4
 
 # Citation Information
 
 If you use this package for in a publication, I would greatly appreciate
-it if you cited it. You can get the citation information by typing this
-into R:
-
-    citation('logitr')
+it if you cited it. You can get the citation information by typing
+`citation("logitr")` into R:
+
+``` r
+citation("logitr")
+#> 
+#> To cite logitr in publications use:
+#> 
+#>   John Paul Helveston. logitr: Random utility logit models with
+#>   preference and willingness to pay space parameterizations (2020)
+#> 
+#> A BibTeX entry for LaTeX users is
+#> 
+#>   @Manual{,
+#>     title = {logitr: Random utility logit models with preference and willingness to pay space parameterizations},
+#>     author = {John Paul Helveston},
+#>     year = {2020},
+#>     note = {R package version 0.0.4},
+#>     url = {https://jhelvy.github.io/logitr/},
+#>   }
+```

README.md

@@ -16,3 +16,26 @@ help(package = 'logitr')
 
 # Install from github
 # devtools::install_github('jhelvy/logitr')
+
+
+# Make hex sticker --------------------
+
+library(hexSticker)
+library(latex2exp)
+library(ggplot2)
+library(showtext)
+p <- ggplot() +
+    theme_void() +
+    theme_transparent() +
+    annotate(
+        geom = "text", x = 0, y = 0,
+        label = TeX('$\\hat{\\omega} = \\frac{\\hat{\\beta}}{\\hat{\\alpha}}$'),
+        size = 6)
+font_add_google("Fira Sans Condensed")
+showtext_auto()
+sticker(p,
+    package = "logitr",
+    h_fill = "#d9d9d9", h_color = "#007bff",
+    p_color = "black", p_size = 10,
+    s_x = 1, s_y = .75, s_width = 1, s_height=1,
+    p_family = "Fira Sans Condensed", filename = "man/figures/logitr-hex.png")

build.R

@@ -5,7 +5,7 @@ citEntry(
   title    = "logitr: Random utility logit models with preference and willingness to pay space parameterizations",
   author   = "John Paul Helveston",
   year     = "2020",
-  note     = "R package version 1.2.0",
+  note     = "R package version 0.0.4",
   url      = "https://jhelvy.github.io/logitr/",
   textVersion = "John Paul Helveston. logitr: Random utility logit models with preference and willingness to pay space parameterizations (2020)"
 )