Added examples and CRAN requirements.

.Rbuildignore

@@ -8,3 +8,4 @@
 ^\.github$
 ^README\.Rmd$
 ^codecov\.yml$
+^cran-comments\.md$

DESCRIPTION

@@ -1,8 +1,10 @@
 Package: nixtlar
-Title: R SDK For Nixtla's TimeGPT
+Title: R SDK for Nixtla's TimeGPT
 Version: 1.0.0
-Authors@R: 
-    person("Mariana", "Menchero", , "[email protected]", role = c("aut", "cre"))
+Authors@R: c(
+    person("Mariana", "Menchero", email = "[email protected]", role = c("aut", "cre"), comment = "First author and maintainer"),
+    person("Nixtla", role = "cph", comment = "Copyright held by Nixtla")
+    )
 Description: An SDK to interface with Nixtla's TimeGPT from R. 
     It provides functions for setting and validating tokens and for generating forecasts with TimeGPT via API calls.
     Compatible with the tsibble package and base R. 

R/date_conversion.R

@@ -5,6 +5,12 @@
 #' @return A list with the inferred frequency and df with the new index.
 #' @export
 #'
+#' @examples
+#' df <- AirPassengers
+#' tsbl <- tsibble::as_tsibble(df)
+#' names(tsbl) <- c("ds", "y")
+#' date_conversion(tsbl)
+#'
 date_conversion <- function(df){
   cls <- class(df$ds)[1]
 

R/get_token.R

@@ -4,6 +4,10 @@
 #' @return If available, the TIMEGTP_TOKEN
 #' @export
 #' @keywords internal
+#' @examples
+#' \dontrun{
+#'   .get_token() # Assumes token was previously set successfully
+#' }
 #'
 .get_token <- function(){
 

R/infer_frequency.R

@@ -5,6 +5,10 @@
 #' @return The inferred frequency.
 #' @export
 #'
+#' @examples
+#' df <- nixtlar::electricity
+#' infer_frequency(df)
+#'
 infer_frequency <- function(df){
   freq <- NULL
   dates <- sort(unique(df$ds))

R/nixtla_set_token.R

@@ -5,6 +5,11 @@
 #' @return A message indicating the token has been set in the global environment.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#' }
+#'
 nixtla_set_token <- function(token) {
   options("TIMEGPT_TOKEN"=token)
   message("Token has been set for the current session.")

R/nixtla_validate_token.R

@@ -3,6 +3,12 @@
 #' @return A message indicating whether the token is valid.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   nixtlar::nixtla_validate_token
+#' }
+#'
 nixtla_validate_token <- function(){
 
   token <- .get_token()

R/timegpt_anomaly_detection.R

@@ -12,6 +12,13 @@
 #' @return A tsibble or a data frame with the anomalies detected in the historical period.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   df <- nixtlar::electricity
+#'   fcst <- nixtlar::timegpt_anomaly_detection(df, id_col="unique_id")
+#' }
+#'
 timegpt_anomaly_detection <- function(df, freq=NULL, id_col=NULL, time_col="ds", target_col="y", level=c(99), clean_ex_first=TRUE, model="timegpt-1"){
 
   # Prepare data ----

R/timegpt_cross_validation.R

@@ -17,6 +17,13 @@
 #' @return A tsibble or a data frame with TimeGPT's cross validation.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   df <- nixtlar::electricity
+#'   fcst <- nixtlar::timegpt_cross_validation(df, h = 8, id_col = "unique_id", n_windows = 5)
+#' }
+#'
 timegpt_cross_validation <- function(df, h=8, freq=NULL, id_col=NULL, time_col="ds", target_col="y", X_df=NULL, level=NULL, n_windows=1, step_size=NULL, finetune_steps=0, clean_ex_first=TRUE, model="timegpt-1"){
 
   # Prepare data ----

R/timegpt_data_prep.R

@@ -11,6 +11,10 @@
 #' @export
 #' @keywords internal
 #'
+#' @examples
+#' df <- nixtlar::electricity
+#' data <- .timegpt_data_prep(df, freq="H")
+#'
 .timegpt_data_prep <- function(df, freq, id_col, time_col, target_col){
 
   if(!tsibble::is_tsibble(df) & !is.data.frame(df)){

R/timegpt_forecast.R

@@ -16,6 +16,13 @@
 #' @return TimeGPT's forecast.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   df <- nixtlar::electricity
+#'   fcst <- nixtlar::timegpt_forecast(df, h=8, id_col="unique_id", level=c(80,95))
+#' }
+#'
 timegpt_forecast <- function(df, h=8, freq=NULL, id_col=NULL, time_col="ds", target_col="y", X_df=NULL, level=NULL, finetune_steps=0, clean_ex_first=TRUE, add_history=FALSE, model="timegpt-1"){
 
   # Prepare data ----

R/timegpt_historic.R

@@ -12,6 +12,13 @@
 #' @return TimeGPT's forecast for the in-sample period.
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   df <- nixtlar::electricity
+#'   fcst <- nixtlar::timegpt_historic(df, id_col="unique_id", level=c(80,95))
+#' }
+#'
 timegpt_historic <- function(df, freq=NULL, id_col=NULL, time_col="ds", target_col="y", level=NULL, finetune_steps=0, clean_ex_first=TRUE){
 
   # Prepare data ----

R/timegpt_plot.R

@@ -13,6 +13,14 @@
 #' @return Plot with the forecast and insample values
 #' @export
 #'
+#' @examples
+#' \dontrun{
+#'   nixtlar::nixtla_set_token("YOUR_TOKEN")
+#'   df <- nixtlar::electricity
+#'   fcst <- nixtlar::timegpt_forecast(df, h=8, id_col="unique_id", level=c(80,95))
+#'   nixtlar::timegpt_plot(df, fcst, h=8, id_col="unique_id")
+#' }
+#'
 timegpt_plot <- function(df, fcst=NULL, h=NULL, id_col=NULL, time_col="ds", target_col="y", unique_ids = NULL, max_insample_length=NULL, plot_anomalies=FALSE){
 
   if(!tsibble::is_tsibble(df) & !is.data.frame(df)){

README.Rmd

@@ -25,7 +25,7 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![CRAN status](https://www.r-pkg.org/badges/version/nixtlar)](https://CRAN.R-project.org/package=nixtlar)
-[![R-CMD-check](https://github.com/MMenchero/nixtlar/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/MMenchero/nixtlar/actions/workflows/R-CMD-check.yaml)
+[![R-CMD-check](https://github.com/Nixtla/nixtlar/actions/workflows/R-CMD-check.yaml)](https://github.com/Nixtla/nixtlar/actions/workflows/R-CMD-check.yaml)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://www.apache.org/licenses/LICENSE-2.0)
 <!-- badges: end -->
 

README.md

@@ -8,14 +8,14 @@
 
 [![CRAN
 status](https://www.r-pkg.org/badges/version/nixtlar)](https://CRAN.R-project.org/package=nixtlar)
-[![R-CMD-check](https://github.com/MMenchero/nixtlar/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/MMenchero/nixtlar/actions/workflows/R-CMD-check.yaml)
+[![R-CMD-check](https://github.com/Nixtla/nixtlar/actions/workflows/R-CMD-check.yaml)](https://github.com/Nixtla/nixtlar/actions/workflows/R-CMD-check.yaml)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://www.apache.org/licenses/LICENSE-2.0)
 <!-- badges: end -->
 
 The `nixtlar` package provides R users with a SDK for [Nixtla’s
 TimeGPT](https://docs.nixtla.io/).
 
-## Your gateway to TimeGPT
+### Your gateway to TimeGPT
 
 TimeGPT is a cutting-edge generative pre-trained transformer model that
 has been trained on the largest collection of publicly available time
@@ -24,7 +24,7 @@ democratizes forecasting, enabling rapid and accurate predictions, even
 for datasets not seen during training. Learn more about TimeGPT
 [here](https://arxiv.org/abs/2310.03589).
 
-## Installation
+### Installation
 
 You can install the development version of `nixtlar` from
 [GitHub](https://github.com/) with:
@@ -34,38 +34,34 @@ You can install the development version of `nixtlar` from
 devtools::install_github("Nixtla/nixtlar")
 ```
 
-## Example
+### Example
 
 Get started with TimeGPT now.
 
 ``` r
 library(nixtlar)
 
 # Load sample dataset 
-df <- nixtlar::electricity # can also be a tsibble! 
+df <- nixtlar::electricity # this can also be a tsibble!
 
 # Set TIMEGPT Token 
 nixtlar::nixtla_set_token("TIMEGPT TOKEN")
-#> Token has been set for the current session.
 
-# Forecast the next 8 horizons using TimeGPT
+# Forecast the next 8 steps using TimeGPT
 fcst <- nixtlar::timegpt_forecast(df, h = 8, id_col = "unique_id", level = c(80,95))
-#> Frequency chosen: H
 
 # Plot TimeGPT forecast 
 nixtlar::timegpt_plot(df, fcst, h = 8, id_col = "unique_id", max_insample_length = 100)
-#> Frequency chosen: H
 ```
 
-<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />
+Using TimeGPT through `nixtlar` requires a token provided by Nixtla. To
+see how to obtain it and learn about different ways to set it up, please
+read the [Get
+Started](https://nixtla.github.io/nixtlar/articles/get-started.html)
+guide. Here, you can also find a more detailed explanation of how the
+forecast and plot functions work.
 
-Using TIMEGPT through `nixtlar` requires a token provided by Nixtla. To
-see how to get it and different ways to set it up, please read the [Get
-Started](https://nixtla.github.io/nixtlar/articles/get-started.html).
-Here you can also find a more detailed explanation on how `nixtlar`’s
-forecast and plot function work.
-
-## Main features of `nixtlar`
+### Main features of `nixtlar`
 
 - Works with both data frames and
   [tsibbles](https://tsibble.tidyverts.org/).
@@ -82,10 +78,10 @@ forecast and plot function work.
 
 - Includes its own plot function.
 
-Please read the **Articles** on this website or check out the
-**Reference** to understand how you can use all these features.
+Please read the **Articles** or check out the **Reference** to
+understand how you can use all these features.
 
-## Python SDK
+### Python SDK
 
 Are you a Python user? If yes, then check out the [Python SDK for
 TimeGPT](https://github.com/Nixtla/nixtla).

cran-comments.md

@@ -0,0 +1,5 @@
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.