# March 2022 list of packages of interest

- Introduction
- cOde: Automated C Code Generation for 'deSolve', 'bvpSolve'
- speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets
- MSclassifR: Automated Classification of Mass Spectra
- ggdist: Visualizations of Distributions and Uncertainty
- StatMatch: Statistical Matching or Data Fusion
- latex2exp: Use LaTeX Expressions in Plots
- tablet: Tabulate Descriptive Statistics in Multiple Formats
- Mychisq: Chi-Squared Test for Goodness of Fit and Independence Test
- cooltools: Practical Tools for Scientific Computations and Visualizations
- waves: Vis-NIR Spectral Analysis Wrapper

*TOC*

- cOde: Automated C Code Generation for 'deSolve', 'bvpSolve'
- speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets
- MSclassifR: Automated Classification of Mass Spectra
- ggdist: Visualizations of Distributions and Uncertainty
- StatMatch: Statistical Matching or Data Fusion
- latex2exp: Use LaTeX Expressions in Plots
- tablet: Tabulate Descriptive Statistics in Multiple Formats
- Mychisq: Chi-Squared Test for Goodness of Fit and Independence Test
- cooltools: Practical Tools for Scientific Computations and Visualizations
- waves: Vis-NIR Spectral Analysis Wrapper

# Introduction

Each month I will describe the package that I've discovered or rediscovered and the ones that I've used the most of my time. I will start with the package used in my work and the the one that I would like to try/did not had time to use for work and also fun

Each card is organised as this

## Name of the package: short description

*mytags*: #example tag

*links*

[cran package link]

[cran vignette link]

[github link]

*description from the author/vignette*

*mynotes*

# cOde: Automated C Code Generation for 'deSolve', 'bvpSolve'

*mytags*: #C #Jacobians

*links*

[cran package link] https://cran.r-project.org/package=cOde

*description from the author/vignette*

Generates all necessary C functions allowing the user to work with the compiled-code interface of ode() and bvptwp(). The implementation supports "forcings" and "events". Also provides functions to symbolically compute Jacobians, sensitivity equations and adjoint sensitivities being the basis for sensitivity analysis.

*mynotes*

# speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets

*mytags*: #fitting #GLM

*links*

[cran package link] https://cran.r-project.org//package=speedglm

*description from the author/vignette*

Fitting linear models and generalized linear models to large data sets by updating algorithms.

*mynotes*

# MSclassifR: Automated Classification of Mass Spectra

*mytags*: #Classification #Mass-Spectra
*links*

[cran package link] https://cran.r-project.org//package=MSclassifR

[vignette link] https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobia.html

*description from the author/vignette*

Functions to classify mass spectra in known categories, and to determine discriminant mass-over-charge values. It includes easy-to-use functions for pre-processing mass spectra, functions to determine discriminant mass-over-charge values (m/z) from a library of mass spectra corresponding to different categories, and functions to predict the category (species, phenotypes, etc.) associated to a mass spectrum from a list of selected mass-over-charge values. Two vignettes illustrating how to use the functions of this package from real data sets are also available online to help users:https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Ecrobia.html and https://agodmer.github.io/MSclassifR_examples/Vignettes/Vignettemsclassifr_Klebsiella.html.

mynotes

# ggdist: Visualizations of Distributions and Uncertainty

*mytags*: #ggplot #Distributions #Uncertainty
*links*

[cran package link] https://cran.r-project.org/package=ggdist

[vignette link] https://cran.r-project.org/web/packages/ggdist/vignettes/dotsinterval.html

https://cran.r-project.org/web/packages/ggdist/vignettes/freq-uncertainty-vis.html

https://cran.r-project.org/web/packages/ggdist/vignettes/lineribbon.html

https://cran.r-project.org/web/packages/ggdist/vignettes/slabinterval.html

*description from the author/vignette*

Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to:points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html, density plots, gradient plots, dot plots (Wilkinson L., 1999) doi:10.1080/00031305.1999.10474474, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) doi:10.1145/2858036.2858558, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) doi:10.1145/3173574.3173718, and fit curves with multiple uncertainty ribbons.

mynotes

# StatMatch: Statistical Matching or Data Fusion

*mytags*: #data fusion #matching
*links*

[cran package link] https://cran.r-project.org/package=StatMatch

[github link] https://github.com/marcellodo/StatMatch, https://github.com/marcellodo/StatMatch/tree/master/Tutorials_Vignette_OtherDocs

*description from the author/vignette*

Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing with data from complex sample surveys are available too.

*mynotes*

# latex2exp: Use LaTeX Expressions in Plots

*mytags*: #LaTeX #plotting
*links*

[cran package link] https://cran.r-project.org/package=latex2exp

[github link] https://github.com/stefano-meschiari/latex2exp/issues

*description from the author/vignette*

Parses and converts LaTeX math formulas to R's plotmath expressions, used to enter mathematical formulas and symbols to be rendered as text, axis labels, etc. throughout R's plotting system.

*mynotes*

# tablet: Tabulate Descriptive Statistics in Multiple Formats

*mytags*: #LaTeX #plotting
*links*

[cran package link] https://cran.r-project.org/package=tablet

[vignette link] https://cran.r-project.org/web/packages/tablet/vignettes/tablet-introduction-html.html

*description from the author/vignette*

Creates a table of descriptive statistics for factor and numeric columns in a data frame. Displays these by groups, if any. Highly customizable, with support for 'html' and 'pdf' provided by 'kableExtra'. Respects original column order, column labels, and factor level order. See ?tablet.data.frame and vignettes.

mynotes

# Mychisq: Chi-Squared Test for Goodness of Fit and Independence Test

*mytags*: #LaTeX #plotting
*links*

[cran package link] https://cran.r-project.org/package=Mychisq

*description from the author/vignette*

The chi-squared test for goodness of fit and independence test.

# cooltools: Practical Tools for Scientific Computations and Visualizations

*mytags*: #Visualizations #Plots
*links*

[cran package link] https://cran.r-project.org/package=cooltools

*description from the author/vignette*

Collection of routines for efficient scientific computations in physics and astrophysics. These routines include utility functions, advanced computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) doi:10.1086/172900), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included are a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020) doi:10.1093/mnras/staa445. These routines include special color scales, projection functions, and bitmap handling routines.

# waves: Vis-NIR Spectral Analysis Wrapper

*mytags*: #spectra #processing
*links*

[cran package link] https://cran.r-project.org/package=cooltools

[github link] https://github.com/GoreLab/waves/

*description from the author/vignette*

Originally designed application in the context of resource-limited plant research and breeding programs, 'waves' provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal doi:10.1002/ppj2.20012. Specialized cross-validation schemes are described in detail in Jarquín et al. (2017) doi:10.3835/plantgenome2016.12.0130. Example data is from Ikeogu et al. (2017) doi:10.1371/journal.pone.0188918.