Detection of Structural Changes in Climate and Environment Time Series

Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Compiles and Visualizes Wildfire, Climate, and Air Quality Data

Fetches data from three disparate data sources and allows user to perform analyses on them. It offers two core components: 1. A robust data retrieval and preparation infrastructure for wildfire, climate, and air quality index data and 2. A simple, informative, and interactive visualizations of the aforementioned datasets for California counties from 2011 through 2015. The sources of data are: wildfire data from Kaggle < https://www.kaggle.com/rtatman/188-million-us-wildfires>, climate data from the National Oceanic and Atmospheric Administration < https://www.ncdc.noaa.gov/cdo-web/token>, and air quality data from the Environmental Protection Agency < https://aqs.epa.gov/aqsweb/documents/data_api.html>.

DMR Detection by Non-Homogeneous Hidden Markov Model from Methylation Array Data

Perform differential analysis for methylation array data. Detect differentially methylated regions (DMRs) from array M-values. The core is a Non-homogeneous Hidden Markov Model for estimating spatial correlation and a novel Constrained Gaussian Mixture Model for modeling the M-value pairs of each individual locus.

Tools for Analyzing Climate Extremes

Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) .

Access Data from the Oregon State Prism Climate Project

Allows users to access the Oregon State Prism climate data (< https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

Climate Services' Indicators Based on Sub-Seasonal to Decadal Predictions

Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, health...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in Pérez-Zanón et al. (2023) and was developed in the context of the H2020 projects MED-GOLD (776467) and S2S4E (776787) projects, as well as the Horizon Europe project MEDEWSA (101121192) and the national project BOREAS (PID2022-140673OA-I00). See Lledó et al. (2019) and Chou et al., 2023 for details.

Ratios of Coefficients in the General Linear Model

Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model, linear mixed model, and for quantiles in a one-way layout. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) < https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) . Confidence interval estimations for ratios of linear combinations of linear model parameters like in (multiple) slope ratio and parallel line assays can be carried out. Moreover, it is possible to calculate the sample sizes required in comparisons with a control based on relative margins. For the simple two-sample problem, functions for a t-test for ratio-formatted hypotheses and the corresponding confidence interval are provided assuming homogeneous or heterogeneous group variances.

Climate Water Balance for Irrigation Purposes

Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) ; or Hargreaves and Samani (1985) . Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well.

Tools to Visualize CM SAF NetCDF Data

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (< https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The 'cmsafvis' R-package provides a collection of R-operators for the analysis and visualization of CM SAF NetCDF data. CM SAF climate data records are provided for free via (< https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (< http://www.cmsaf.eu/R_toolbox>).