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diff --git a/README.md b/README.md
@@ -2,11 +2,17 @@
 
 > The time series toolkit for Python
 
-This library is currently under development and is not intended for general usage yet. Functionality is experimental until release 0.1.0.
-
 **Please ⭐ us on GitHub (it takes 2-seconds and means a lot).**
 
-# Install Dev Version
+# Installation
+
+Install the latest stable version of `pytimetk` using `pip`:
+
+```bash
+pip install pytimetk
+```
+
+Alternatively you can install the development version:
 
 ```bash
 pip install git+https://github.com/business-science/pytimetk.git

diff --git a/docs/_freeze/getting-started/01_installation/execute-results/html.json b/docs/_freeze/getting-started/01_installation/execute-results/html.json
@@ -1,7 +1,7 @@
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-    "markdown": "---\ntitle: Install\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\n---\n\n::: {.callout-warning collapse=\"false\"}\n## Under Development\n\nThis library is currently under development and is not intended for general usage yet. Functionality is experimental until release 0.1.0. \n:::\n\n# Quick Install\n\nLet's get you up and running with `timetk` fast. You can install from GitHub with this code. \n\n```bash\npip install git+https://github.com/business-science/pytimetk.git\n```\n\n# Next steps\n\nCheck out the [Quick Start Guide Next.](/getting-started/02_quick_start.html)\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `timetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n",
+    "markdown": "---\ntitle: Install\ntoc: true\ntoc-depth: 3\nnumber-sections: true\nnumber-depth: 2\n---\n\n<!-- ::: {.callout-warning collapse=\"false\"}\n## Under Development\n\nThis library is currently under development and is not intended for general usage yet. Functionality is experimental until release 0.1.0. \n::: -->\n\n# Quick Install\n\nLet's get you up and running with `pytimetk` fast with the latest stable release. \n\n```bash\npip install pytimetk\n```\n\nYou can install from GitHub with this code. \n\n```bash\npip install git+https://github.com/business-science/pytimetk.git\n```\n\n# Next steps\n\nCheck out the [Quick Start Guide Next.](/getting-started/02_quick_start.html)\n\n# More Coming Soon...\n\nWe are in the early stages of development. But it's obvious the potential for `pytimetk` now in Python. 🐍\n\n- Please [⭐ us on GitHub](https://github.com/business-science/pytimetk) (it takes 2-seconds and means a lot). \n- To make requests, please see our [Project Roadmap GH Issue #2](https://github.com/business-science/pytimetk/issues/2). You can make requests there. \n- Want to contribute? [See our contributing guide here.](/contributing.html) \n\n",
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       "01_installation_files"
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diff --git a/docs/_freeze/reference/floor_date/execute-results/html.json b/docs/_freeze/reference/floor_date/execute-results/html.json
@@ -1,7 +1,7 @@
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-    "markdown": "---\ntitle: floor_date\n---\n\n\n\n`floor_date(idx, unit='D')`\n\nRound a date down to the specified unit (e.g. Flooring).\n\nThe `floor_date` function takes a pandas Series of dates and returns a new Series with the dates rounded down to the specified unit.\n\n## Parameters\n\n| Name   | Type                          | Description                                                                                                                                                                                                                                                            | Default    |\n|--------|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `idx`  | pd.Series or pd.DatetimeIndex | The `idx` parameter is a pandas Series or pandas DatetimeIndex object that contains datetime values. It represents the dates that you want to round down.                                                                                                              | _required_ |\n| `unit` | str                           | The `unit` parameter in the `floor_date` function is a string that specifies the time unit to which the dates in the `idx` series should be rounded down. It has a default value of \"D\", which stands for day. Other possible values for the `unit` parameter could be | `'D'`      |\n\n## Returns\n\n| Type      | Description                                                                                |\n|-----------|--------------------------------------------------------------------------------------------|\n| pd.Series | The `floor_date` function returns a pandas Series object containing datetime64[ns] values. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport timetk as tk\nimport pandas as pd\n\ndates = pd.date_range(\"2020-01-01\", \"2020-01-10\", freq=\"1H\")\ndates\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\nDatetimeIndex(['2020-01-01 00:00:00', '2020-01-01 01:00:00',\n               '2020-01-01 02:00:00', '2020-01-01 03:00:00',\n               '2020-01-01 04:00:00', '2020-01-01 05:00:00',\n               '2020-01-01 06:00:00', '2020-01-01 07:00:00',\n               '2020-01-01 08:00:00', '2020-01-01 09:00:00',\n               ...\n               '2020-01-09 15:00:00', '2020-01-09 16:00:00',\n               '2020-01-09 17:00:00', '2020-01-09 18:00:00',\n               '2020-01-09 19:00:00', '2020-01-09 20:00:00',\n               '2020-01-09 21:00:00', '2020-01-09 22:00:00',\n               '2020-01-09 23:00:00', '2020-01-10 00:00:00'],\n              dtype='datetime64[ns]', length=217, freq='H')\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Works on DateTimeIndex\ntk.floor_date(dates, unit=\"D\")\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\n0     2020-01-01\n1     2020-01-01\n2     2020-01-01\n3     2020-01-01\n4     2020-01-01\n         ...    \n212   2020-01-09\n213   2020-01-09\n214   2020-01-09\n215   2020-01-09\n216   2020-01-10\nName: idx, Length: 217, dtype: datetime64[ns]\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Works on Pandas Series\ndates.to_series().floor_date(unit=\"D\")\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n2020-01-01 00:00:00   2020-01-01\n2020-01-01 01:00:00   2020-01-01\n2020-01-01 02:00:00   2020-01-01\n2020-01-01 03:00:00   2020-01-01\n2020-01-01 04:00:00   2020-01-01\n                         ...    \n2020-01-09 20:00:00   2020-01-09\n2020-01-09 21:00:00   2020-01-09\n2020-01-09 22:00:00   2020-01-09\n2020-01-09 23:00:00   2020-01-09\n2020-01-10 00:00:00   2020-01-10\nFreq: H, Length: 217, dtype: datetime64[ns]\n```\n:::\n:::\n\n\n",
+    "markdown": "---\ntitle: floor_date\n---\n\n\n\n`floor_date(idx, unit='D')`\n\nRound a date down to the specified unit (e.g. Flooring).\n\nThe `floor_date` function takes a pandas Series of dates and returns a new Series with the dates rounded down to the specified unit.\n\n## Parameters\n\n| Name   | Type                          | Description                                                                                                                                                                                                                                                            | Default    |\n|--------|-------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|\n| `idx`  | pd.Series or pd.DatetimeIndex | The `idx` parameter is a pandas Series or pandas DatetimeIndex object that contains datetime values. It represents the dates that you want to round down.                                                                                                              | _required_ |\n| `unit` | str                           | The `unit` parameter in the `floor_date` function is a string that specifies the time unit to which the dates in the `idx` series should be rounded down. It has a default value of \"D\", which stands for day. Other possible values for the `unit` parameter could be | `'D'`      |\n\n## Returns\n\n| Type      | Description                                                                                |\n|-----------|--------------------------------------------------------------------------------------------|\n| pd.Series | The `floor_date` function returns a pandas Series object containing datetime64[ns] values. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pytimetk as tk\nimport pandas as pd\n\ndates = pd.date_range(\"2020-01-01\", \"2020-01-10\", freq=\"1H\")\ndates\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\nDatetimeIndex(['2020-01-01 00:00:00', '2020-01-01 01:00:00',\n               '2020-01-01 02:00:00', '2020-01-01 03:00:00',\n               '2020-01-01 04:00:00', '2020-01-01 05:00:00',\n               '2020-01-01 06:00:00', '2020-01-01 07:00:00',\n               '2020-01-01 08:00:00', '2020-01-01 09:00:00',\n               ...\n               '2020-01-09 15:00:00', '2020-01-09 16:00:00',\n               '2020-01-09 17:00:00', '2020-01-09 18:00:00',\n               '2020-01-09 19:00:00', '2020-01-09 20:00:00',\n               '2020-01-09 21:00:00', '2020-01-09 22:00:00',\n               '2020-01-09 23:00:00', '2020-01-10 00:00:00'],\n              dtype='datetime64[ns]', length=217, freq='H')\n```\n:::\n:::\n\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\n# Works on DateTimeIndex\ntk.floor_date(dates, unit=\"D\")\n```\n\n::: {.cell-output .cell-output-display execution_count=2}\n```\n0     2020-01-01\n1     2020-01-01\n2     2020-01-01\n3     2020-01-01\n4     2020-01-01\n         ...    \n212   2020-01-09\n213   2020-01-09\n214   2020-01-09\n215   2020-01-09\n216   2020-01-10\nName: idx, Length: 217, dtype: datetime64[ns]\n```\n:::\n:::\n\n\n::: {.cell execution_count=3}\n``` {.python .cell-code}\n# Works on Pandas Series\ndates.to_series().floor_date(unit=\"D\")\n```\n\n::: {.cell-output .cell-output-display execution_count=3}\n```\n2020-01-01 00:00:00   2020-01-01\n2020-01-01 01:00:00   2020-01-01\n2020-01-01 02:00:00   2020-01-01\n2020-01-01 03:00:00   2020-01-01\n2020-01-01 04:00:00   2020-01-01\n                         ...    \n2020-01-09 20:00:00   2020-01-09\n2020-01-09 21:00:00   2020-01-09\n2020-01-09 22:00:00   2020-01-09\n2020-01-09 23:00:00   2020-01-09\n2020-01-10 00:00:00   2020-01-10\nFreq: H, Length: 217, dtype: datetime64[ns]\n```\n:::\n:::\n\n\n",
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diff --git a/docs/_freeze/reference/future_frame/execute-results/html.json b/docs/_freeze/reference/future_frame/execute-results/html.json
diff --git a/docs/_freeze/reference/get_available_datasets/execute-results/html.json b/docs/_freeze/reference/get_available_datasets/execute-results/html.json
@@ -1,7 +1,7 @@
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-    "markdown": "---\ntitle: get_available_datasets\n---\n\n\n\n`datasets.get_datasets.get_available_datasets()`\n\nGet a list of 12 datasets that can be loaded with `timetk.load_dataset`.\n\nThe `get_available_datasets` function returns a sorted list of available dataset names from the `timetk.datasets` module. The available datasets are:\n\n## Returns\n\n| Type   | Description                                                                                                               |\n|--------|---------------------------------------------------------------------------------------------------------------------------|\n| list   | The function `get_available_datasets` returns a sorted list of available dataset names from the `timetk.datasets` module. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport timetk as tk\n\ntk.get_available_datasets()\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n['bike_sales_sample',\n 'bike_sharing_daily',\n 'm4_daily',\n 'm4_hourly',\n 'm4_monthly',\n 'm4_quarterly',\n 'm4_weekly',\n 'm4_yearly',\n 'stocks_daily',\n 'taylor_30_min',\n 'walmart_sales_weekly',\n 'wikipedia_traffic_daily']\n```\n:::\n:::\n\n\n",
+    "markdown": "---\ntitle: get_available_datasets\n---\n\n\n\n`datasets.get_datasets.get_available_datasets()`\n\nGet a list of 12 datasets that can be loaded with `pytimetk.load_dataset`.\n\nThe `get_available_datasets` function returns a sorted list of available dataset names from the `pytimetk.datasets` module. The available datasets are:\n\n## Returns\n\n| Type   | Description                                                                                                                 |\n|--------|-----------------------------------------------------------------------------------------------------------------------------|\n| list   | The function `get_available_datasets` returns a sorted list of available dataset names from the `pytimetk.datasets` module. |\n\n## Examples\n\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nimport pytimetk as tk\n\ntk.get_available_datasets()\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```\n['bike_sales_sample',\n 'bike_sharing_daily',\n 'm4_daily',\n 'm4_hourly',\n 'm4_monthly',\n 'm4_quarterly',\n 'm4_weekly',\n 'm4_yearly',\n 'stocks_daily',\n 'taylor_30_min',\n 'walmart_sales_weekly',\n 'wikipedia_traffic_daily']\n```\n:::\n:::\n\n\n",
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diff --git a/docs/_freeze/reference/get_holiday_signature/execute-results/html.json b/docs/_freeze/reference/get_holiday_signature/execute-results/html.json