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Statistical and Machine Learning Engine for Long-Term Natural Resource Management Data
A comprehensive toolkit for statistical and machine learning-based
analysis of long-term Natural Resource Management (NRM) datasets. Integrates
formula-driven approaches, statistical inference, and machine learning (ML)
models for advanced analytics. Modules cover trend and structural analysis
(Mann-Kendall test, slope estimation, Chow test, structural break detection),
multivariate system modelling (Partial Least Squares (PLS), Structural
Equation Modelling (SEM)), response curve optimisation, time-series
forecasting (Autoregressive Integrated Moving Average (ARIMA), hybrid
models), panel data and treatment effects (Difference-in-Differences (DiD),
causal machine learning), uncertainty and sensitivity analysis (bootstrap,
Monte Carlo, Bayesian), and automated model selection and performance
comparison. Designed for long-term datasets covering soil, water, crop, and
climate domains. Key references: Mann and Kendall (1945)
Testing Linear Regression Models
A collection of tests, data sets, and examples for diagnostic checking in linear regression models. Furthermore, some generic tools for inference in parametric models are provided.
Feed-Forward Neural Networks and Multinomial Log-Linear Models
Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models.
Multi-Model Inference
Tools for model selection and model averaging with support for a wide range of statistical models. Automated model selection through subsets of the maximum model, with optional constraints for model inclusion. Averaging of model parameters and predictions based on model weights derived from information criteria (AICc and alike) or custom model weighting schemes.
Complete Environment for Bayesian Inference
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).
Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Construct Modeling Packages
Building modeling packages is hard. A large amount of effort generally goes into providing an implementation for a new method that is efficient, fast, and correct, but often less emphasis is put on the user interface. A good interface requires specialized knowledge about S3 methods and formulas, which the average package developer might not have. The goal of 'hardhat' is to reduce the burden around building new modeling packages by providing functionality for preprocessing, predicting, and validating input.
Tools for Working with Posterior Distributions
Provides useful tools for both users and developers of packages
for fitting Bayesian models or working with output from Bayesian models.
The primary goals of the package are to:
(a) Efficiently convert between many different useful formats of
draws (samples) from posterior or prior distributions.
(b) Provide consistent methods for operations commonly performed on draws,
for example, subsetting, binding, or mutating draws.
(c) Provide various summaries of draws in convenient formats.
(d) Provide lightweight implementations of state of the art posterior
inference diagnostics. References: Vehtari et al. (2021)
The Knockoff Filter for Controlled Variable Selection
The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.