diff --git a/modules/assim.sequential/inst/covariates.R b/modules/assim.sequential/inst/covariates.R
index 52e86ec88fd..99ea5ab585b 100644
--- a/modules/assim.sequential/inst/covariates.R
+++ b/modules/assim.sequential/inst/covariates.R
@@ -99,7 +99,7 @@ NA_SoilGrids <- terra::crop(SoilGrids, NA_extent)
 names(NA_SoilGrids) <- c("phh2o", "nitrogen", "soc", "sand")
 
 # Resample SoilGrids to match WorldClim maps
-NA_SoilGrids <- terra::resample(NA_WorldClim, NA_SoilGrids, method = 'bilinear') # made change
+NA_SoilGrids <- terra::resample(NA_SoilGrids, NA_WorldClim, method = 'bilinear')
 
 # Resample land cover to match WorldClim maps (~25 min)
 land_cover <- terra::resample(land_cover, NA_WorldClim, method = 'near')
diff --git a/modules/data.mining/DESCRIPTION b/modules/data.mining/DESCRIPTION
index 7ac5389be56..0816ff34964 100644
--- a/modules/data.mining/DESCRIPTION
+++ b/modules/data.mining/DESCRIPTION
@@ -21,4 +21,4 @@ LazyLoad: yes
 LazyData: FALSE
 Collate:
 Encoding: UTF-8
-RoxygenNote: 7.1.2
+RoxygenNote: 7.3.1
diff --git a/modules/uncertainty/R/sensitivity.analysis.R b/modules/uncertainty/R/sensitivity.analysis.R
index c66992b8623..69f162d970f 100644
--- a/modules/uncertainty/R/sensitivity.analysis.R
+++ b/modules/uncertainty/R/sensitivity.analysis.R
@@ -10,14 +10,13 @@
 ##' Spline estimate of univariate relationship between parameter value and model output
 ##'
 ##' Creates a spline function using the splinefun function that estimates univariate response of parameter input to model output
-##' @name sa.splinefun
-##' @title Sensitivity spline function 
-##' @param quantiles.input 
-##' @param quantiles.output 
+##'
+##' @param quantiles.input passed to `x` argument of `stats::splinefun`
+##' @param quantiles.output passed to `y` argument of `stats::splinefun`
 ##' @export
-##' @return function   
+##' @return function
 sa.splinefun <- function(quantiles.input, quantiles.output) {
-  return(stats::splinefun(quantiles.input, quantiles.output, method = "monoH.FC"))
+  return(stats::splinefun(x = quantiles.input, y = quantiles.output, method = "monoH.FC"))
 } # sa.splinefun
 
 
@@ -25,8 +24,6 @@ sa.splinefun <- function(quantiles.input, quantiles.output) {
 ##' Calculates the standard deviation of the variance estimate
 ##'
 ##' Uses the equation \eqn{\sigma^4\left(\frac{2}{n-1}+\frac{\kappa}{n}\right)}{\sigma^4 (2/(n-1) + \kappa/n)}
-##' @name sd.var
-##' @title Standard deviation of sample variance
 ##' @param x sample
 ##' @return estimate of standard deviation of the sample variance
 ##' @export
@@ -42,17 +39,15 @@ sd.var <- function(x) {
 ##'
 ##' Note that this calculates the 'excess kurtosis', which is defined as kurtosis - 3.
 ##' This statistic is used in the calculation of the standard deviation of sample variance
-##' in the function \code{\link{sd.var}}.  
-##' Additional details 
-##' @name kurtosis
-##' @title Calculate excess kurtosis from a vector
+##' in the function \code{\link{sd.var}}.
+##'
 ##' @param x vector of values
 ##' @return numeric value of kurtosis
 ##' @author David LeBauer
 ##' @references  NIST/SEMATECH e-Handbook of Statistical Methods, \url{http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm}, 2011-06-20.
 kurtosis <- function(x) {
-  kappa <- sum((x - mean(x, na.rm = TRUE)) ^ 4) / 
-    ((sum(!is.na(x)) - 1) * stats::sd(x, na.rm = TRUE) ^ 4) - 3
+  kappa <- sum((x - mean(x, na.rm = TRUE))^4) /
+    ((sum(!is.na(x)) - 1) * stats::sd(x, na.rm = TRUE)^4) - 3
   return(kappa)
 } # kurtosis
 # ==================================================================================================#
@@ -64,10 +59,9 @@ kurtosis <- function(x) {
 ##' This function evaluates the sensitivity of a model to a parameter.
 ##' This is done by evaluating the first derivative of the univariate spline estimate
 ##' of the model response at the parameter median.
-##' @name get.sensitivity
-##' @title Calculate Sensitivity
-##' @param trait.samples 
-##' @param sa.splinefun 
+##'
+##' @param trait.samples parameter values to evaluate at their median
+##' @param sa.splinefun fitted spline function. Must take two arguments.
 ##' @export
 ##' @return numeric estimate of model sensitivity to parameter
 get.sensitivity <- function(trait.samples, sa.splinefun) {
@@ -77,10 +71,10 @@ get.sensitivity <- function(trait.samples, sa.splinefun) {
 
 
 #--------------------------------------------------------------------------------------------------#
+##' Get coefficient of variance
+##'
 ##' Given a set of numbers (a numeric vector), this returns the set's coefficient of variance.
 ##'
-##' @name get.coef.var
-##' @title Get coefficient of variance 
 ##' @param set numeric vector of trait values
 ##' @export
 ##' @return coeficient of variance
@@ -93,70 +87,90 @@ get.coef.var <- function(set) {
 ##' Generic function for the elasticity
 ##'
 ##' Given the sensitivity, samples, and outputs for a single trait, return elasticity
-##' @name get.elasticity
-##' @title Get Elasticity 
+##'
 ##' @param sensitivity univariate sensitivity of model to a parameter, can be calculated by \code{\link{get.sensitivity}}
 ##' @param samples samples from trait distribution
 ##' @param outputs model output from ensemble runs
 ##' @export
-##' @return elasticity = normalized sensitivity 
+##' @return elasticity = normalized sensitivity
 get.elasticity <- function(sensitivity, samples, outputs) {
   return(sensitivity / (stats::median(outputs) / stats::median(samples)))
 } # get.elasticity
 
 
 #--------------------------------------------------------------------------------------------------#
-##' Performs univariate sensitivity analysis and variance decomposition 
+##' Performs univariate sensitivity analysis and variance decomposition
 ##'
-##' This function estimates the univariate responses of a model to a parameter for a set of traits, calculates the model sensitivity at the median, and performs a variance decomposition. This function results in a set of sensitivity plots (one per variable) and plot_variance_decomposition.
-##' @name sensitivity.analysis
-##' @title Sensitivity Analysis 
-##' @param trait.samples list of vectors, one per trait, representing samples of the trait value, with length equal to the mcmc chain length. Samples are taken from either the prior distribution or meta-analysis results
-##' @param sa.samples data.frame with one column per trait and one row for the set of quantiles used in sensitivity analysis. Each cell contains the value of the trait at the given quantile.
-##' @param sa.output  list of data.frames, similar to sa.samples, except cells contain the results of a model run with that trait x quantile combination and all other traits held at their median value  
+##' This function estimates the univariate responses of a model to a parameter for a set of traits, calculates the model sensitivity at the median,
+##'   and performs a variance decomposition. This function results in a set of sensitivity plots (one per variable) and plot_variance_decomposition.
+##'
+##' @param trait.samples list of vectors, one per trait, representing samples of the trait value, with length equal to the mcmc chain length.
+##'   Samples are taken from either the prior distribution or meta-analysis results
+##' @param sa.samples data.frame with one column per trait and one row for the set of quantiles used in sensitivity analysis.
+##    Each cell contains the value of the trait at the given quantile.
+##' @param sa.output  list of data.frames, similar to sa.samples, except cells contain the results of a model run
+##'   with that trait x quantile combination and all other traits held at their median value
 ##' @param outdir directory to which plots are written
 ##' @return results of sensitivity analysis
 ##' @export
 ##' @author David LeBauer
 ##' @examples
 ##' \dontrun{
-##'sensitivity.analysis(
+##' sensitivity.analysis(
 ##'  trait.samples[[pft$name]],
-##'  sa.samples[[pft$name]], 
-##'  sa.agb[[pft$name]], 
+##'  sa.samples[[pft$name]],
+##'  sa.agb[[pft$name]],
 ##'  pft$outdir
-##')
+##' )
 ##' }
 sensitivity.analysis <- function(trait.samples, sa.samples, sa.output, outdir) {
   traits <- names(trait.samples)
-  sa.splines <- sapply(traits,
-                       function(trait) sa.splinefun(sa.samples[[trait]], sa.output[[trait]]))
-  
-  spline.estimates <- lapply(traits, 
-                             function(trait) spline.truncate(sa.splines[[trait]](trait.samples[[trait]])))
+  sa.splines <- sapply(
+    traits,
+    function(trait) sa.splinefun(sa.samples[[trait]], sa.output[[trait]])
+  )
+
+  spline.estimates <- lapply(
+    traits,
+    function(trait) spline.truncate(sa.splines[[trait]](trait.samples[[trait]]))
+  )
   names(spline.estimates) <- traits
-  sensitivities <- sapply(traits, 
-                          function(trait) get.sensitivity(trait.samples[[trait]], sa.splines[[trait]]))
-  elasticities <- sapply(traits, 
-                         function(trait) get.elasticity(sensitivities[[trait]], 
-                                                        trait.samples[[trait]], 
-                                                        spline.estimates[[trait]]))
+  sensitivities <- sapply(
+    traits,
+    function(trait) get.sensitivity(trait.samples[[trait]], sa.splines[[trait]])
+  )
+  elasticities <- sapply(
+    traits,
+    function(trait) {
+      get.elasticity(
+        sensitivities[[trait]],
+        trait.samples[[trait]],
+        spline.estimates[[trait]]
+      )
+    }
+  )
   variances <- sapply(traits, function(trait) stats::var(spline.estimates[[trait]]))
   partial.variances <- variances / sum(variances)
-  
+
   coef.vars <- sapply(trait.samples, get.coef.var)
-  outlist <- list(sensitivity.output = list(sa.samples = sa.samples, 
-                                            sa.splines = sa.splines), 
-                  variance.decomposition.output = list(coef.vars = coef.vars,
-                                                       elasticities = elasticities, 
-                                                       sensitivities = sensitivities,
-                                                       variances = variances,
-                                                       partial.variances = partial.variances))
+  outlist <- list(
+    sensitivity.output = list(
+      sa.samples = sa.samples,
+      sa.splines = sa.splines
+    ),
+    variance.decomposition.output = list(
+      coef.vars = coef.vars,
+      elasticities = elasticities,
+      sensitivities = sensitivities,
+      variances = variances,
+      partial.variances = partial.variances
+    )
+  )
   return(outlist)
 } # sensitivity.analysis
 
 
-##' Truncate spline at zero if..
+##' Truncate spline at zero if...
 ##'
 ##' Truncate spline at zero if P[x<0] < pnorm(-3)
 ##' pnorm(-3) chosen as default value for min quantile
@@ -164,7 +178,7 @@ sensitivity.analysis <- function(trait.samples, sa.samples, sa.output, outdir) {
 ##' sensitivity analysis.
 ##' This parameter could be determined based on minimum value in
 ##' settings$sensitivity.analysis$quantiles
-##' @title Truncate spline 
+##'
 ##' @param x vector
 ##' @param min.quantile threshold quantile for testing lower bound on variable
 ##' @return either x or a vector with values < 0 converted to zero
@@ -172,7 +186,7 @@ sensitivity.analysis <- function(trait.samples, sa.samples, sa.output, outdir) {
 ##' @export
 ##' @examples
 ##' set.seed(0)
-##' x <- c(rgamma(998,1,1), rnorm(10)) 
+##' x <- c(rgamma(998,1,1), rnorm(10))
 ##' min(x) # -0.5238
 ##' min(PEcAn.uncertainty::spline.truncate(x))
 spline.truncate <- function(x, min.quantile = stats::pnorm(-3)) {
diff --git a/modules/uncertainty/man/get.elasticity.Rd b/modules/uncertainty/man/get.elasticity.Rd
index 54d7bbc4727..502a29a5245 100644
--- a/modules/uncertainty/man/get.elasticity.Rd
+++ b/modules/uncertainty/man/get.elasticity.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{get.elasticity}
 \alias{get.elasticity}
-\title{Get Elasticity}
+\title{Generic function for the elasticity}
 \usage{
 get.elasticity(sensitivity, samples, outputs)
 }
@@ -17,8 +17,5 @@ get.elasticity(sensitivity, samples, outputs)
 elasticity = normalized sensitivity
 }
 \description{
-Generic function for the elasticity
-}
-\details{
 Given the sensitivity, samples, and outputs for a single trait, return elasticity
 }
diff --git a/modules/uncertainty/man/get.sensitivity.Rd b/modules/uncertainty/man/get.sensitivity.Rd
index e2c136cef8f..0763edc5706 100644
--- a/modules/uncertainty/man/get.sensitivity.Rd
+++ b/modules/uncertainty/man/get.sensitivity.Rd
@@ -2,22 +2,19 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{get.sensitivity}
 \alias{get.sensitivity}
-\title{Calculate Sensitivity}
+\title{Calculate the sensitivity of a function at the median}
 \usage{
 get.sensitivity(trait.samples, sa.splinefun)
 }
 \arguments{
-\item{trait.samples}{}
+\item{trait.samples}{parameter values to evaluate at their median}
 
-\item{sa.splinefun}{}
+\item{sa.splinefun}{fitted spline function. Must take two arguments.}
 }
 \value{
 numeric estimate of model sensitivity to parameter
 }
 \description{
-Calculate the sensitivity of a function at the median
-}
-\details{
 This function evaluates the sensitivity of a model to a parameter.
 This is done by evaluating the first derivative of the univariate spline estimate
 of the model response at the parameter median.
diff --git a/modules/uncertainty/man/kurtosis.Rd b/modules/uncertainty/man/kurtosis.Rd
index 06b69f1bd50..ae913824d7f 100644
--- a/modules/uncertainty/man/kurtosis.Rd
+++ b/modules/uncertainty/man/kurtosis.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{kurtosis}
 \alias{kurtosis}
-\title{Calculate excess kurtosis from a vector}
+\title{Calculates the excess kurtosis of a vector}
 \usage{
 kurtosis(x)
 }
@@ -13,13 +13,9 @@ kurtosis(x)
 numeric value of kurtosis
 }
 \description{
-Calculates the excess kurtosis of a vector
-}
-\details{
 Note that this calculates the 'excess kurtosis', which is defined as kurtosis - 3.
 This statistic is used in the calculation of the standard deviation of sample variance
-in the function \code{\link{sd.var}}.  
-Additional details
+in the function \code{\link{sd.var}}.
 }
 \references{
 NIST/SEMATECH e-Handbook of Statistical Methods, \url{http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm}, 2011-06-20.
diff --git a/modules/uncertainty/man/sa.splinefun.Rd b/modules/uncertainty/man/sa.splinefun.Rd
index b8d27f71f3e..5e9b788ec11 100644
--- a/modules/uncertainty/man/sa.splinefun.Rd
+++ b/modules/uncertainty/man/sa.splinefun.Rd
@@ -2,21 +2,18 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{sa.splinefun}
 \alias{sa.splinefun}
-\title{Sensitivity spline function}
+\title{Spline estimate of univariate relationship between parameter value and model output}
 \usage{
 sa.splinefun(quantiles.input, quantiles.output)
 }
 \arguments{
-\item{quantiles.input}{}
+\item{quantiles.input}{passed to `x` argument of `stats::splinefun`}
 
-\item{quantiles.output}{}
+\item{quantiles.output}{passed to `y` argument of `stats::splinefun`}
 }
 \value{
 function
 }
 \description{
-Spline estimate of univariate relationship between parameter value and model output
-}
-\details{
 Creates a spline function using the splinefun function that estimates univariate response of parameter input to model output
 }
diff --git a/modules/uncertainty/man/sd.var.Rd b/modules/uncertainty/man/sd.var.Rd
index fe6c570e436..a1f3845d1c6 100644
--- a/modules/uncertainty/man/sd.var.Rd
+++ b/modules/uncertainty/man/sd.var.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{sd.var}
 \alias{sd.var}
-\title{Standard deviation of sample variance}
+\title{Calculates the standard deviation of the variance estimate}
 \usage{
 sd.var(x)
 }
@@ -13,9 +13,6 @@ sd.var(x)
 estimate of standard deviation of the sample variance
 }
 \description{
-Calculates the standard deviation of the variance estimate
-}
-\details{
 Uses the equation \eqn{\sigma^4\left(\frac{2}{n-1}+\frac{\kappa}{n}\right)}{\sigma^4 (2/(n-1) + \kappa/n)}
 }
 \references{
diff --git a/modules/uncertainty/man/sensitivity.analysis.Rd b/modules/uncertainty/man/sensitivity.analysis.Rd
index 4d1cffaf233..563ff528ceb 100644
--- a/modules/uncertainty/man/sensitivity.analysis.Rd
+++ b/modules/uncertainty/man/sensitivity.analysis.Rd
@@ -2,16 +2,18 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{sensitivity.analysis}
 \alias{sensitivity.analysis}
-\title{Sensitivity Analysis}
+\title{Performs univariate sensitivity analysis and variance decomposition}
 \usage{
 sensitivity.analysis(trait.samples, sa.samples, sa.output, outdir)
 }
 \arguments{
-\item{trait.samples}{list of vectors, one per trait, representing samples of the trait value, with length equal to the mcmc chain length. Samples are taken from either the prior distribution or meta-analysis results}
+\item{trait.samples}{list of vectors, one per trait, representing samples of the trait value, with length equal to the mcmc chain length.
+Samples are taken from either the prior distribution or meta-analysis results}
 
-\item{sa.samples}{data.frame with one column per trait and one row for the set of quantiles used in sensitivity analysis. Each cell contains the value of the trait at the given quantile.}
+\item{sa.samples}{data.frame with one column per trait and one row for the set of quantiles used in sensitivity analysis.}
 
-\item{sa.output}{list of data.frames, similar to sa.samples, except cells contain the results of a model run with that trait x quantile combination and all other traits held at their median value}
+\item{sa.output}{list of data.frames, similar to sa.samples, except cells contain the results of a model run
+with that trait x quantile combination and all other traits held at their median value}
 
 \item{outdir}{directory to which plots are written}
 }
@@ -19,17 +21,15 @@ sensitivity.analysis(trait.samples, sa.samples, sa.output, outdir)
 results of sensitivity analysis
 }
 \description{
-Performs univariate sensitivity analysis and variance decomposition
-}
-\details{
-This function estimates the univariate responses of a model to a parameter for a set of traits, calculates the model sensitivity at the median, and performs a variance decomposition. This function results in a set of sensitivity plots (one per variable) and plot_variance_decomposition.
+This function estimates the univariate responses of a model to a parameter for a set of traits, calculates the model sensitivity at the median,
+  and performs a variance decomposition. This function results in a set of sensitivity plots (one per variable) and plot_variance_decomposition.
 }
 \examples{
 \dontrun{
 sensitivity.analysis(
  trait.samples[[pft$name]],
- sa.samples[[pft$name]], 
- sa.agb[[pft$name]], 
+ sa.samples[[pft$name]],
+ sa.agb[[pft$name]],
  pft$outdir
 )
 }
diff --git a/modules/uncertainty/man/spline.truncate.Rd b/modules/uncertainty/man/spline.truncate.Rd
index a1ee6b3518e..3e3dd08eea9 100644
--- a/modules/uncertainty/man/spline.truncate.Rd
+++ b/modules/uncertainty/man/spline.truncate.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/sensitivity.analysis.R
 \name{spline.truncate}
 \alias{spline.truncate}
-\title{Truncate spline}
+\title{Truncate spline at zero if...}
 \usage{
 spline.truncate(x, min.quantile = stats::pnorm(-3))
 }
@@ -15,9 +15,6 @@ spline.truncate(x, min.quantile = stats::pnorm(-3))
 either x or a vector with values < 0 converted to zero
 }
 \description{
-Truncate spline at zero if..
-}
-\details{
 Truncate spline at zero if P[x<0] < pnorm(-3)
 pnorm(-3) chosen as default value for min quantile
 because this is the default low end of range for the
@@ -27,7 +24,7 @@ settings$sensitivity.analysis$quantiles
 }
 \examples{
 set.seed(0)
-x <- c(rgamma(998,1,1), rnorm(10)) 
+x <- c(rgamma(998,1,1), rnorm(10))
 min(x) # -0.5238
 min(PEcAn.uncertainty::spline.truncate(x))
 }