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Data Table Back-End for 'dplyr'
Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' is to allow you to write 'dplyr' code that is automatically translated to the equivalent, but usually much faster, data.table code.
Analyzing Dendrometer Data
Various functions to import, verify, process and plot
high-resolution dendrometer data using daily and stem-cycle approaches
as described in Deslauriers et al, 2007
R Code for Mark Analysis
An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at < http://www.phidot.org/software/mark/downloads/> but is not open source.
"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012)
Various Functions to Facilitate Visualization of Data and Analysis
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
Classification and Regression Tests
Provides wrapper functions for running classification and regression tests using different machine learning techniques, such as Random Forests and decision trees. The package provides standardized methods for preparing data to suit the algorithm's needs, training a model, making predictions, and evaluating results. Also, some functions are provided to run multiple instances of a test.
Using CF-Compliant Calendars with Climate Projection Data
Support for all calendars as specified in the Climate and Forecast (CF) Metadata Conventions for climate and forecasting data. The CF Metadata Conventions is widely used for distributing files with climate observations or projections, including the Coupled Model Intercomparison Project (CMIP) data used by climate change scientists and the Intergovernmental Panel on Climate Change (IPCC). This package specifically allows the user to work with any of the CF-compliant calendars (many of which are not compliant with POSIXt). The CF time coordinate is formally defined in the CF Metadata Conventions document available at < https://cfconventions.org/Data/cf-conventions/cf-conventions-1.12/cf-conventions.html#time-coordinate>.
Minimization Methods for Ill-Conditioned Problems
Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023),
Gene Cluster Visualizations
Provides tools for plotting gene clusters and transcripts by
importing data from GenBank, FASTA, and GFF files. It performs BLASTP and
MUMmer alignments [Altschul et al. (1990)
'shiny' Cron Expression Input Widget
A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. Note that this does not edit the 'crontab' file, it is just an input element for the schedules. See < https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.