finnts - Microsoft Finance Time Series Forecasting Framework

Automated time series forecasting developed by Microsoft Finance. The Microsoft Finance Time Series Forecasting Framework, aka Finn, can be used to forecast any component of the income statement, balance sheet, or any other area of interest by finance. Any numerical quantity over time, Finn can be used to forecast it. While it can be applied outside of the finance domain, Finn was built to meet the needs of financial analysts to better forecast their businesses within a company, and has a lot of built in features that are specific to the needs of financial forecasters. Happy forecasting!

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agentaibusinessfeature-selectionfinancefinntsforecastingllmmachine-learningmicrosofttime-series

10.18 score 259 stars 42 scripts 835 downloads

lightgbm - Light Gradient Boosting Machine

Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.

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cppopenmp

9.43 score 1 stars 11 dependents 24k scripts 6.8k downloads

wpa - Tools for Analysing and Visualising Viva Insights Data

Opinionated functions that enable easier and faster analysis of Viva Insights data. There are three main types of functions in 'wpa': (i) Standard functions create a 'ggplot' visual or a summary table based on a specific Viva Insights metric; (2) Report Generation functions generate HTML reports on a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover more specific applications (e.g. Subject Line text mining) of Viva Insights data. This package adheres to 'tidyverse' principles and works well with the pipe syntax. 'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for simplicity.

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workplace-analytics

6.90 score 32 stars 1 dependents 42 scripts 1.4k downloads

vivainsights - Analyze and Visualize Data from 'Microsoft Viva Insights'

Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.

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6.88 score 15 stars 111 scripts 452 downloads

vivaglint - Analysis Tools for 'Viva Glint' Survey Data

Provides functions for importing, validating, and analyzing 'Viva Glint' survey data exports, with optional API-based import via the 'Microsoft Graph' API. Includes tools for data reshaping, question-level analysis, multi-cycle comparisons, organizational hierarchy analysis, factor analysis, and correlation analysis. Harman (1960, ISBN: 0226316513); Husser (2017) <doi:10.1002/9781118901731.iecrm0048>.

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4.60 score 1 stars 3 scripts 470 downloads