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irinagain

iglu:Interpreting Glucose Data from Continuous Glucose Monitors

Implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.

Maintained by Irina Gaynanova. Last updated 24 days ago.

26 stars 9.00 score 39 scripts

cran

fqacalc:Calculate Floristic Quality Assessment Metrics

A collection of functions for calculating Floristic Quality Assessment (FQA) metrics using regional FQA databases that have been approved or approved with reservations as ecological planning models by the U.S. Army Corps of Engineers (USACE). For information on FQA see Spyreas (2019) <doi:10.1002/ecs2.2825>. These databases are stored in a sister R package, 'fqadata'. Both packages were developed for the USACE by the U.S. Army Engineer Research and Development Center’s Environmental Laboratory.

Maintained by Iris Foxfoot. Last updated 2 years ago.

2.70 score

cran

hydropeak:Detect and Characterize Sub-Daily Flow Fluctuations

An important environmental impact on running water ecosystems is caused by hydropeaking - the discontinuous release of turbine water because of peaks of energy demand. An event-based algorithm is implemented to detect flow fluctuations referring to increase events (IC) and decrease events (DC). For each event, a set of parameters related to the fluctuation intensity is calculated. The framework is introduced in Greimel et al. (2016) "A method to detect and characterize sub-daily flow fluctuations" <doi:10.1002/hyp.10773> and can be used to identify different fluctuation types according to the potential source: e.g., sub-daily flow fluctuations caused by hydropeaking, rainfall, or snow and glacier melt. This is a companion to the package 'hydroroute', which is used to detect and follow hydropower plant-specific hydropeaking waves at the sub-catchment scale and to describe how hydropeaking flow parameters change along the longitudinal flow path as proposed and validated in Greimel et al. (2022).

Maintained by Bettina Grün. Last updated 2 years ago.

2.48 score 1 dependents

sandipgarai

AllMetrics:Calculating Multiple Performance Metrics of a Prediction Model

Provides a function to calculate multiple performance metrics for actual and predicted values. In total eight metrics will be calculated for particular actual and predicted series. Helps to describe a Statistical model's performance in predicting a data. Also helps to compare various models' performance. The metrics are Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), Mean absolute Error (MAE), Mean absolute percentage error (MAPE), Mean Absolute Scaled Error (MASE), Nash-Sutcliffe Efficiency (NSE), Willmott’s Index (WI), and Legates and McCabe Index (LME). Among them, first five are expected to be lesser whereas, the last three are greater the better. More details can be found from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202> and Garai et al. (2024) <doi:10.1007/s11063-024-11552-w>.

Maintained by Dr. Sandip Garai. Last updated 1 years ago.

1.78 score 2 dependents

mrc-ide

rincewind:Utilities for processing forecasts matrices

This package contains utility functions for processing forecasts matrices.

Maintained by Sangeeta Bhatia. Last updated 3 years ago.

1 stars 1.00 score 1 scripts

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