An approach to age-depth modelling that uses Bayesian statistics to reconstruct accumulation histories for 210Pb-dated deposits using prior information. It can combine 210Pb, radiocarbon, and other dates in the chronologies. See Aquino et al. (2018) <doi:10.1007/s13253-018-0328-7>. Note that parts of the code underlying 'rplum' are derived from the 'rbacon' package by the same authors, and there remains a degree of overlap between the two packages.

person( "Benjamin", "Stocker", email = "benjamin.stocker@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2697-9096") )
Implements the P-model (Stocker et al., 2019 <doi:10.5194/gmd-2019-200>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
Benjamin Stocker [aut, cre] (<https://orcid.org/0000-0003-2697-9096>)

Implements the P-model
(Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>),
predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis,
assimilation, and dark respiration rates as a function of the environment
(temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
c(
person(
family = "Stocker",
given = "Benjamin",
email = "benjamin.stocker@gmail.com",
comment = c(ORCID = "0000-0002-5070-8109"),
role = c("aut", "cre")),
person(
family = "Hufkens",
given = "Koen",
email = "koen.hufkens@gmail.com",
role = c("ctb"),
comment = c(ORCID = "0000-0002-5070-8109"))
)
ggplot2, dplyr, purrr, tidyr, knitr, testthat, covr, markdown

Recursive Partitioning for Modeling Survey Data
person("daniell", "toth", email="danielltoth@yahoo.com",
role=c("aut", "cre"))
Fits a linear model to survey data in each node obtained by
recursively partitioning the data.  The splitting variables and splits
selected are obtained using a procedure which adjusts for complex sample
design features used to obtain the data. Likewise the model fitting
algorithm produces design-consistent coefficients to the least squares
linear model between the dependent and independent variables.
The first stage of the design is accounted for in the provided variance
estimates. The main function returns the resulting binary tree with the
linear model fit at every endnode as an R object of class "rpms". The
package provides a number of functions and methods for this rpms class.