Changed path of mock files, updated README, and improved website.

README.Rmd

@@ -4,7 +4,13 @@ output: github_document
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-```{r, include = FALSE}
+```{r setup, include=FALSE}
+library(httptest2)
+start_vignette("mocks")
+
+options("TIMEGPT_TOKEN"="dummy_token")
+options(digits=7)
+
 knitr::opts_chunk$set(
   collapse = TRUE,
   comment = "#>",
@@ -13,10 +19,6 @@ knitr::opts_chunk$set(
 )
 ```
 
-# nixtlar
-
-The `nixtlar` package provides R users with a SDK for [Nixtla's TimeGPT API](https://docs.nixtla.io/). 
-
 <!-- logo -->
 # nixtlar <a href="https://nixtla.github.io/nixtlar/"><img src="man/figures/logo.png" align="right" height="139" alt="nixtlar website" /></a>
 
@@ -26,49 +28,64 @@ The `nixtlar` package provides R users with a SDK for [Nixtla's TimeGPT API](htt
 [![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/).
 
-## 🔄 nixtlar: Your Gateway to TimeGPT from R
+### Your gateway to TimeGPT
 
-With `nixtlar` you can easily interact with TimeGPT through simple API calls, making the power of TimeGPT readily accessible in your projects. Learn more about TimeGPT [here](https://arxiv.org/abs/2310.03589).  
+TimeGPT is a cutting-edge generative pre-trained transformer model that has been trained on the largest collection of publicly available time series data. Accessible to R users via the `nixtlar package`, TimeGPT 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:
 
-``` r
+```{r eval=FALSE}
 # install.packages("devtools")
-#devtools::install_github("MMenchero/nixtlar")
+devtools::install_github("Nixtla/nixtlar")
 ```
 
-## 🎈Quick Start
+### Example 
 
-To use TimeGPT, you first need to request a token from Nixtla. You can do this at  https://dashboard.nixtla.io/. 
+Get started with TimeGPT now. 
 
-Once you have the token, you'll need to set it up in your R session. The `nixtlar` package provides a function to do this.
 ```{r}
-#library(nixtlar)
+library(nixtlar)
 
-# Set up token 
-#set_token("YOUR_TOKEN") 
-```
+# Load sample dataset 
+df <- nixtlar::electricity # this can also be a tsibble! 
 
-You'll need to set the token every time you re-start your R session. `nixtlar` also includes a function to validate the token. 
+# Set TIMEGPT Token 
+nixtlar::nixtla_set_token("TIMEGPT TOKEN")
 
-```{r}
-# Validate token
-#validate_token("YOUR_TOKEN") 
+# Forecast the next 8 steps using TimeGPT
+fcst <- nixtlar::timegpt_forecast(df, h = 8, id_col = "unique_id", level = c(80,95))
+
+# Plot TimeGPT forecast 
+nixtlar::timegpt_plot(df, fcst, h = 8, id_col = "unique_id", max_insample_length = 100)
 ```
 
-You don't need to validate the token every time you set it up, only when you want to ensure its validity. 
+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.
 
-`nixtlar provides` an easy-to-use function to call the TimeGPT API. It works with both [tsibbles](https://tsibble.tidyverts.org/) and base R data frames. 
+### Main features of `nixtlar`
 
-```{r}
-# Call the TimeGPT API 
-#df <- tssible::as_tsibble(AirPassengers) # df can also be a data frame  
+- Works with both data frames and [tsibbles](https://tsibble.tidyverts.org/). 
 
-#timegpt_forecast(df, h=8, freq=12, time_col="index", target_col="value")
-```
+- Allows you to use TimeGPT's main features, including:  
+  - Anomaly detection 
+  - Exogenous variables 
+  - Prediction intervals 
+  - Finetuning 
+  - Multiple time series 
+  - Historical forecast 
+  - Cross-validation 
+
+- Includes its own plot function. 
+
+Please read the **Articles** or check out the **Reference** to understand how you can use all these features. 
 
-## 🐍 Python SDK
-Are you a Python user? If yes, then check out [NixtlaTS](https://github.com/Nixtla/nixtla), a Python SDK for TimeGPT and check out the [documentation](https://docs.nixtla.io/docs). 
+### Python SDK 
+
+Are you a Python user? If yes, then check out the [Python SDK for TimeGPT](https://github.com/Nixtla/nixtla). 
+
+```{r, include=FALSE}
+end_vignette()
+```

README.md

@@ -1,11 +1,5 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-
-# nixtlar
-
-The `nixtlar` package provides R users with a SDK for [Nixtla’s TimeGPT
-API](https://docs.nixtla.io/).
-
 <!-- logo -->
 
 # nixtlar <a href="https://nixtla.github.io/nixtlar/"><img src="man/figures/logo.png" align="right" height="139" alt="nixtlar website" /></a>
@@ -18,61 +12,80 @@ status](https://www.r-pkg.org/badges/version/nixtlar)](https://CRAN.R-project.or
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://www.apache.org/licenses/LICENSE-2.0)
 <!-- badges: end -->
 
-## 🔄 nixtlar: Your Gateway to TimeGPT from R
+The `nixtlar` package provides R users with a SDK for [Nixtla’s
+TimeGPT](https://docs.nixtla.io/).
+
+## Your gateway to TimeGPT
 
-With `nixtlar` you can easily interact with TimeGPT through simple API
-calls, making the power of TimeGPT readily accessible in your projects.
-Learn more about TimeGPT [here](https://arxiv.org/abs/2310.03589).
+TimeGPT is a cutting-edge generative pre-trained transformer model that
+has been trained on the largest collection of publicly available time
+series data. Accessible to R users via the `nixtlar package`, TimeGPT
+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:
 
 ``` r
 # install.packages("devtools")
-#devtools::install_github("MMenchero/nixtlar")
+devtools::install_github("Nixtla/nixtlar")
 ```
 
-## 🎈Quick Start
+## Example
 
-To use TimeGPT, you first need to request a token from Nixtla. You can
-do this at <https://dashboard.nixtla.io/>.
-
-Once you have the token, you’ll need to set it up in your R session. The
-`nixtlar` package provides a function to do this.
+Get started with TimeGPT now.
 
 ``` r
-#library(nixtlar)
+library(nixtlar)
 
-# Set up token 
-#set_token("YOUR_TOKEN") 
-```
+# Load sample dataset 
+df <- nixtlar::electricity # can also be a tsibble! 
 
-You’ll need to set the token every time you re-start your R session.
-`nixtlar` also includes a function to validate the token.
+# Set TIMEGPT Token 
+nixtlar::nixtla_set_token("TIMEGPT TOKEN")
+#> Token has been set for the current session.
 
-``` r
-# Validate token
-#validate_token("YOUR_TOKEN") 
+# Forecast the next 8 horizons 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
 ```
 
-You don’t need to validate the token every time you set it up, only when
-you want to ensure its validity.
+<img src="man/figures/README-unnamed-chunk-2-1.png" width="100%" />
 
-`nixtlar provides` an easy-to-use function to call the TimeGPT API. It
-works with both [tsibbles](https://tsibble.tidyverts.org/) and base R
-data frames.
+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.
 
-``` r
-# Call the TimeGPT API 
-#df <- tssible::as_tsibble(AirPassengers) # df can also be a data frame  
+## Main features of `nixtlar`
 
-#timegpt_forecast(df, h=8, freq=12, time_col="index", target_col="value")
-```
+- Works with both data frames and
+  [tsibbles](https://tsibble.tidyverts.org/).
+
+- Allows you to use TimeGPT’s main features, including:
+
+  - Anomaly detection
+  - Exogenous variables
+  - Prediction intervals
+  - Finetuning
+  - Multiple time series
+  - Historical forecast
+  - Cross-validation
+
+- 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.
 
-## 🐍 Python SDK
+## Python SDK
 
-Are you a Python user? If yes, then check out
-[NixtlaTS](https://github.com/Nixtla/nixtla), a Python SDK for TimeGPT
-and check out the [documentation](https://docs.nixtla.io/docs).
+Are you a Python user? If yes, then check out the [Python SDK for
+TimeGPT](https://github.com/Nixtla/nixtla).

_pkgdown.yml

@@ -21,6 +21,9 @@ navbar:
         - href: articles/cross-validation.html
         - text: "Historical Forecast"
         - href: articles/historical-forecast.html
+  right:
+    - icon: "fa-github"
+      href: https://github.com/Nixtla/nixtlar
 
 home:
   links:

tests/mocks/dashboard.nixtla.io/api/timegpt.json

@@ -0,0 +1,30 @@
+{
+    "data": {
+        "timestamp": [
+            "2016-01-14 00:00:00",
+            "2016-01-15 00:00:00",
+            "2016-01-16 00:00:00",
+            "2016-01-17 00:00:00",
+            "2016-01-18 00:00:00",
+            "2016-01-19 00:00:00",
+            "2016-01-20 00:00:00"
+        ],
+        "value": [
+            7.960582256317139,
+            7.7414960861206055,
+            7.728490352630615,
+            8.267574310302734,
+            8.543140411376953,
+            8.298713684082031,
+            8.105557441711426
+        ],
+        "input_tokens": 43,
+        "output_tokens": 7,
+        "finetune_tokens": 0
+    },
+    "message": "success",
+    "details": "request successful",
+    "code": "B10",
+    "requestID": "CLC9LCNZL6",
+    "support": "If you have questions or need support, please email [email protected]"
+}