CRAN updates: chopin #rstats

Modelado Ordenado con R by Max Kuhn and Julia Silge
#RStats
bigbookofr.com/chapters/español.html

Mosaic chart titled "How You Die Depends on Where You Live" showing cause of death by World Bank income group for 2021. Four columns represent Low income, Lower-middle income, Upper-middle income, and High income countries, with width proportional to total deaths. In Low income, infectious diseases dominate at 50%. As income rises, cardiovascular diseases grow from 16% to 30% and cancers from 7% to 21%. Injuries decrease from 12% to 6%. Lower-middle income has the widest column at 24.1 million deaths. Data from WHO Global Health Estimates.

Day 03 #30DayChartChallenge — Mosaic

How you die depends on where you live.

In low-income countries, 50% of deaths are from infections. In high-income countries, heart disease (30%) and cancer (21%) dominate.

Data: WHO Global Health Estimates 2021
Built with R + ggmosaic

#DataViz #RStats

CRAN updates: fru #rstats

#30DayChartChallenge Day 3 - Mosaic Plot, done in #rstats

For the top 1,000 board games ranked on @boardgamegeek.com, how prevalent are different categories by their publication date? You can see war games lessening over time, and family/strategy games increasing over time.

Using {reticulate} and referencing R objects in Python with r.* and Python objects in R with py$* feels like God mode.

#Rstats #Python

The mosaic chart shows the electricity generation mix by World Bank income group in 2022. Tile width represents each group's share of global output; tile height shows the energy source mix within that group. Upper-middle-income countries — dominated by China — produce the largest share of global electricity at roughly 48%, with fossil fuels accounting for 31% of global output alone. A dashed vertical line marks the boundary between upper-middle and high-income countries, where the energy mix meaningfully diversifies. High-income countries account for about 39% of global production, with notable shares in wind, solar, hydro, and nuclear. Lower-middle-income countries produce around 12%, still heavily fossil-dependent. Low-income nations collectively produce less than 1% of global electricity and are annotated. Data source: Our World in Data, Energy Institute Statistical Review of World Energy.

📊 #30DayChartChallenge 2026 – day 03
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Comparisons | Mosaic
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🔗 : stevenponce.netlify.app/data_visuali...
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#rstats | #r4ds | #dataviz | #ggplot2

From the DSLC.video aRchives:

🔵 Generative AI Handbook: Chapters 11, 12 youtu.be/RiXLq-wY9hM

🔵 Advanced R: R6 youtu.be/z-99vgrcJJM

Support the Data Science Learning Community at patreon.com/DSLC

#dataBS #RStats #GenAI #AI

From the DSLC.video aRchives:

🔵 Advanced R: Introduction youtu.be/vfTg6upHvO4

🔴 The Rust Programming Language: Getting Started & Introduction youtu.be/DLG12TYJtkA

Support the Data Science Learning Community at patreon.com/DSLC

#dataBS #RStats #rustRustLangRustRustLang

🚀 New #rstats 📦: ecoXCorr now available on CRAN!

It provides a simple workflow to explore lagged associations between environmental #timeseries and eco / epidemio responses:
➡️ flexible lag intervals
➡️ GLMM via glmmTMB
➡️ plot cross-correlation maps

see github.com/Nmoiroux/eco...

🌐🧪🌍

#ecology

Mosaic plot showing US census region on the x-axis and aquaculture group (baitfish, food fish, sport fish, ornamental fish, crustaceans, mollusks, and other) on the y-axis as filled rectangles. Rectangles are sized to the sales values ($) per region and type. The South region has the largest width with food fish dominating within the region. Next widest is the West which is primarily mollusks. The Northeast is also dominated by mollusks. Finally, the Midwest is mostly food fish, but baitfish takes a higher percentage than in other regions.

#30DayChartChallenge #Day3 – Mosaic
US aquaculture production by type and region in terms of sales ($).
The {merimekko} R package made this very easy to create.

Shiny: tinyurl.com/6bf3puth

#DataViz #Rstats

yes - notionally Luma has better discoverability compared to meetup, but just not enough events on it yet to make it obvious.

I am trying to get people to post their #rstats events on luma - so we can ditch meetup.

The amounts of money wasted by the R Consortium on meetup is OBSCENE!!

CRAN updates: ILSAstats #rstats

CRAN: Package mpmaggregate Aggregates matrix population models (MPMs) in both the lambda (stable growth rate) and R0 (net reproductive rate) frameworks, including standard and elasticity-consistent aggregators. Standard aggregation in the lambda framework maintains consistent lambda and stable stage distribution, while standard aggregation in the R0 framework maintains consistent R0 and cohort stable stage distribution. Elasticity-consistent aggregators maintain these same consistencies with respect to the chosen framework and additionally preserve consistent reproductive values in the lambda framework and cohort reproductive values in the R0 framework. Aggregation can take the form of general-to-general MPM (mpm_aggregate) or Leslie-to-Leslie MPM (leslie_aggregate).

New CRAN package mpmaggregate with initial version 0.2.5
#rstats
https://cran.r-project.org/package=mpmaggregate

CRAN: Package zmctp Implements zero-modified versions of the Complex 'Tri-Parametric' Pearson distribution for overdispersed count data. The package addresses limitations of existing implementations when the parameter b approaches zero. It provides distribution functions, maximum likelihood estimation, and diagnostic tools for modeling count data with excess zeros. The methodology is based on 'Rodriguez-Avi' and coauthors (2003) &lt;<a href="https://doi.org/10.1007%2Fs00362-002-0134-7" target="_top">doi:10.1007/s00362-002-0134-7</a>&gt;.

New CRAN package zmctp with initial version 0.1.0
#rstats
https://cran.r-project.org/package=zmctp

CRAN: Package tulpaMesh Generate constrained Delaunay triangulation meshes for use with stochastic partial differential equation (SPDE) spatial models (Lindgren, Rue and Lindstroem 2011 &lt;<a href="https://doi.org/10.1111%2Fj.1467-9868.2011.00777.x" target="_top">doi:10.1111/j.1467-9868.2011.00777.x</a>&gt;). Provides automatic mesh generation from point coordinates with boundary constraints, Ruppert refinement for mesh quality, finite element method (FEM) matrix assembly (mass, stiffness, projection), barrier models, spherical meshes via icosahedral subdivision, and metric graph meshes for network geometries. Built on the 'CDT' header-only C++ library (Amirkhanov 2024 &lt;<a href="https://github.com/artem-ogre/CDT" target="_top">https://github.com/artem-ogre/CDT</a>&gt;). Designed as the mesh backend for the 'tulpa' Bayesian hierarchical modelling engine but usable standalone for any spatial triangulation task.

New CRAN package tulpaMesh with initial version 0.1.1
#rstats
https://cran.r-project.org/package=tulpaMesh

CRAN: Package statAfrikR A comprehensive statistical toolbox for National Statistics Institutes (INS) in Africa. Provides functions for survey data import ('KoboToolbox', 'ODK', 'CSPro', 'Excel', 'Stata', 'SPSS'), data processing and validation, weighted statistical analysis (descriptive statistics, cross-tabulations, regression, Human Development Index (HDI), Multidimensional Poverty Index (MPI) following Alkire and Foster (2011) &lt;<a href="https://doi.org/10.1093%2Foep%2Fgpr051" target="_top">doi:10.1093/oep/gpr051</a>&gt;, inequalities), visualization (age pyramids, thematic maps, official charts) and dissemination ('SDMX' export, 'DDI' metadata, anonymization, Word/PDF reports). Designed to work in resource-constrained environments, offline and in French.

New CRAN package statAfrikR with initial version 0.1.0
#rstats
https://cran.r-project.org/package=statAfrikR

CRAN: Package shard Provides a parallel execution runtime for R that emphasizes deterministic memory behavior and efficient handling of large shared inputs. 'shard' enables zero-copy parallel reads via shared/memory-mapped segments, encourages explicit output buffers to avoid large result aggregation, and supervises worker processes to mitigate memory drift via controlled recycling. Diagnostics report peak memory usage, end-of-run memory return, and hidden copy/materialization events to support reproducible performance benchmarking.

New CRAN package shard with initial version 0.1.0
#rstats
https://cran.r-project.org/package=shard

CRAN: Package seroreconstruct A Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) &lt;<a href="https://doi.org/10.1038%2Fs41467-022-29310-8" target="_top">doi:10.1038/s41467-022-29310-8</a>&gt; for methodological details.

New CRAN package seroreconstruct with initial version 1.1.5
#rstats
https://cran.r-project.org/package=seroreconstruct

Object not found! https://cran.r-project.org/package=scip

New CRAN package scip with initial version 1.10.0-2
#rstats
https://cran.r-project.org/package=scip

Object not found! https://cran.r-project.org/package=mlstm

New CRAN package mlstm with initial version 0.1.6
#rstats
https://cran.r-project.org/package=mlstm

CRAN: Package MAIHDA Provides a comprehensive toolkit for conducting Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). Methods are described in Merlo (2018) &lt;<a href="https://doi.org/10.1016%2Fj.socscimed.2017.12.018" target="_top">doi:10.1016/j.socscimed.2017.12.018</a>&gt; and Evans et al. (2018) &lt;<a href="https://doi.org/10.1016%2Fj.socscimed.2017.11.011" target="_top">doi:10.1016/j.socscimed.2017.11.011</a>&gt;. Automatically generates intersectional strata, fits analytical models, extracts statistics, and produces visualizations.

New CRAN package MAIHDA with initial version 0.1.0
#rstats
https://cran.r-project.org/package=MAIHDA

CRAN: Package csmbuilder A collection of tools for designing, implementing, testing, documenting and visualizing dynamic simulation cropping system models. Models are specified as a combination of state variables, parameters, intermediate factors and input data that define a system of ordinary differential equations. Specified models can be used to simulate dynamic processes using numerical integration algorithms.

New CRAN package csmbuilder with initial version 0.1.0
#rstats
https://cran.r-project.org/package=csmbuilder

CRAN: Package birdcolors Create attractive palettes based on the colors of the world's birds. Palettes are composed of 2 to 9 colors, with options to expand palettes via interpolation. Compatible with the package 'ggplot2' and base R graphics.

New CRAN package birdcolors with initial version 1.0.1
#rstats
https://cran.r-project.org/package=birdcolors

Object not found! https://cran.r-project.org/package=balnet

New CRAN package balnet with initial version 0.0.1
#rstats
https://cran.r-project.org/package=balnet

An R Platform for Social Scientists by Burak AYDIN, James ALGINA, Walter LEITE and Hakan ATILGAN
#RStats
bigbookofr.com/chapters/social%20scienc...

Marimekko chart of African countries cross-classified by Plasmodium falciparum incidence rate and household Insecticide-Treated-Net access level in 2025, where the majority of high-burden countries (50% of all countries) also have the highest ITN access.

#30DayChartChallenge #Day3 : Comparisons - Mosaic

Insecticide-Treated Nets are key tools for malaria prevention. This chart shows how accessible they are across malaria-affected African countries.

📊 Created with the {marimekko} R package

#dataviz #rstats #ggplot2

Object not found! https://cran.r-project.org/package=lineagefreq

New CRAN package lineagefreq with initial version 0.2.0
#rstats
https://cran.r-project.org/package=lineagefreq

CRAN removals: GetDFPData snvecR #rstats

#30DayChartChallenge #Day3
Comparisons: Mosaic

Datenquelle: Land Oberösterreich

Tool:
#RStats

Farben: suf_palette("classic") von github.com/alburezg/suf...