* R Interface to ‘ONNX’* (

**onnx**)

R Interface to ‘ONNX’ – Open Neural Network Exchange <https://onnx.ai/>. ‘ONNX’ provides an open source format for machine learning models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

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**Descriptive mAchine Learning EXplanations****DALEX**)

Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack of interpretability. ‘DALEX’ package contains various explainers that help to understand the link between input variables and model output. The single_variable() explainer extracts conditional response of a model as a function of a single selected variable. It is a wrapper over packages ‘pdp’ and ‘ALEPlot’. The single_prediction() explainer attributes arts of model prediction to articular variables used in the model. It is a wrapper over ‘breakDown’ package. The variable_dropout() explainer assess variable importance based on consecutive permutations. All these explainers can be plotted with generic plot() function and compared across different models.

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**Boltzmann Entropy of a Landscape Gradient****belg**)

Calculates the Boltzmann entropy of a landscape gradient. It uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>).

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**Data Analysis Functions for ‘SBpipe’ Package****sbpiper**)

Provides an API for analysing repetitive parameter estimations and simulations of mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs) or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter estimation ‘sbpiper’ calculates statistics and generates plots for parameter density, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can be generated using all or a subset of the best computed parameter sets. Among the analyses for model simulation ‘sbpiper’ calculates statistics and generates plots for deterministic and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model parameters can also be generated. This package is primarily used by the software ‘SBpipe’. Citation: Dalle Pezze P, Le Novère N. SBpipe: a collection of pipelines for automating repetitive simulation and analysis tasks. BMC Systems Biology. 2017;11:46. <doi:10.1186/s12918-017-0423-3>.

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**Robust Calibration of Imperfect Mathematical Models****RobustCalibration**)

Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2017) <arXiv:1707.08215>.