diff --git a/R/Log_Likelihood.R b/R/Log_Likelihood.R
index 6b9fce0..9b66d60 100755
--- a/R/Log_Likelihood.R
+++ b/R/Log_Likelihood.R
@@ -12,8 +12,7 @@
 #'
 #' @examples
 #' @noRd
-negloglik_vecchia_ST <- function(logparms, res, vecchia.approx, param.seq,
-                                 scaling, nscale) {
+negloglik_vecchia_ST <- function(logparms, res, vecchia.approx, param.seq, scaling, nscale) {
   parms <- unlog.params(logparms, param.seq, 1)
   locs.scaled <- vecchia.approx$locsord
   for (j in 1:nscale) {
@@ -141,8 +140,7 @@ mvnegloglik <- function(logparams, vecchia.approx, y, param.seq, P) {
 ##############################################################################
 ### Flexible Spatiotemporal Multivariate Matern Negative Loglikelihood Function ###########
 
-mvnegloglik_ST <- function(logparams, vecchia.approx, y, param.seq, P, scaling,
-                           nscale) {
+mvnegloglik_ST <- function(logparams, vecchia.approx, y, param.seq, P, scaling, nscale) {
   #  Input-
   #  logparams: A numeric vector of length (4*P)+(4*choose(P,2)).
   #             To construct these parameters we unlist a list of the 7 covariance
@@ -235,8 +233,7 @@ mvnegloglik.full <- function(logparams, locs, y, param.seq) {
 }
 
 ##############################################################################
-create.cov.upper.flex <- function(P, marg.var, marg.range, marg.smooth,
-                                  nugget, R.corr) {
+create.cov.upper.flex <- function(P, marg.var, marg.range, marg.smooth, nugget, R.corr) {
   # Create the symmetrical marginal+cross-covariance flexible matern from the
   # given parameters. Output is a list of the 4 Matern parameters as matrices
   sig2.mat <- diag(marg.var, P, P)
@@ -337,8 +334,7 @@ cat.covariances <- function(locs.list, sig2, range, smoothness, nugget) {
 ##############################################################################
 ### Create the likelihood initial values                    #########
 
-create.initial.values.flex <- function(marg.var, marg.range, marg.smooth,
-                                       nugget, R.corr, P) {
+create.initial.values.flex <- function(marg.var, marg.range, marg.smooth, nugget, R.corr, P) {
   # Log-transform the covariance parameters and arrange in the proper order
   # for the likelihood function
   logparams.init <- c(
diff --git a/R/PrestoGP_CreateU_Multivariate.R b/R/PrestoGP_CreateU_Multivariate.R
index c486541..0bdcf24 100755
--- a/R/PrestoGP_CreateU_Multivariate.R
+++ b/R/PrestoGP_CreateU_Multivariate.R
@@ -66,8 +66,7 @@ max_min_ordering <- function(locs, dist_func) {
 #' @param dist_func Any distance function with a signature of dist(query_location, locations_matrix)
 #'
 #' @return A vector containing the indices of the neighbors
-knn_indices <- function(ordered_locs, query, n_neighbors,
-                        dist_func, dist_func_code) {
+knn_indices <- function(ordered_locs, query, n_neighbors, dist_func, dist_func_code) {
   if (dist_func_code == "custom") {
     dists <- dist_func(query, ordered_locs)
     dists_order <- order(dists)
@@ -92,8 +91,7 @@ knn_indices <- function(ordered_locs, query, n_neighbors,
 #'
 #' @return A list containing two matrices, each with one row per location:
 #' an indices matrix with the indices of nearest neighbors for each location, and a distance matrix with the associated distances
-sparseNN <- function(ordered_locs, n_neighbors,
-                     dist_func, dist_func_code, ordered_locs_pred = NULL) {
+sparseNN <- function(ordered_locs, n_neighbors, dist_func, dist_func_code, ordered_locs_pred = NULL) {
   ee <- min(apply(ordered_locs, 2, stats::sd))
   n <- nrow(ordered_locs)
   ordered_locs <- ordered_locs + matrix(
@@ -197,9 +195,9 @@ calc.q <- function(nn.obj, firstind.pred) {
 
 #' @export
 vecchia_Mspecify <- function(locs.list, m, locs.list.pred = NULL,
-                             dist.func = NULL,
-                             ordering.pred = c("obspred", "general"),
-                             pred.cond = c("independent", "general")) {
+  dist.func = NULL,
+  ordering.pred = c("obspred", "general"),
+  pred.cond = c("independent", "general")) {
   ordering.pred <- match.arg(ordering.pred)
   pred.cond <- match.arg(pred.cond)
 
diff --git a/R/PrestoGP_Model.R b/R/PrestoGP_Model.R
index dab605f..62727c0 100755
--- a/R/PrestoGP_Model.R
+++ b/R/PrestoGP_Model.R
@@ -71,16 +71,16 @@ setGeneric("show_theta", function(object, Y_names) standardGeneric("show_theta")
 setGeneric(
   "prestogp_fit",
   function(model, Y, X, locs, scaling = NULL, apanasovich = FALSE,
-           covparams = NULL, beta.hat = NULL, tol = 0.999999, max_iters = 100, verbose = FALSE,
-           optim.method = "Nelder-Mead", optim.control = list(trace = 0, reltol = 1e-3, maxit = 5000),
-           parallel = FALSE, foldid = NULL) {
+    covparams = NULL, beta.hat = NULL, tol = 0.999999, max_iters = 100, verbose = FALSE,
+    optim.method = "Nelder-Mead", optim.control = list(trace = 0, reltol = 1e-3, maxit = 5000),
+    parallel = FALSE, foldid = NULL) {
     standardGeneric("prestogp_fit")
   }
 )
 setGeneric(
   "prestogp_predict",
   function(model, X = "matrix", locs = "matrix", m = "numeric", ordering.pred = c("obspred", "general"),
-           pred.cond = c("independent", "general"), return.values = c("mean", "meanvar")) {
+    pred.cond = c("independent", "general"), return.values = c("mean", "meanvar")) {
     standardGeneric("prestogp_predict")
   }
 )
@@ -215,10 +215,10 @@ setMethod(
 setMethod(
   "prestogp_fit", "PrestoGPModel",
   function(model, Y, X, locs, scaling = NULL, apanasovich = NULL,
-           covparams = NULL, beta.hat = NULL, tol = 0.999999,
-           max_iters = 100, verbose = FALSE, optim.method = "Nelder-Mead",
-           optim.control = list(trace = 0, reltol = 1e-3, maxit = 5000),
-           parallel = FALSE, foldid = NULL) {
+    covparams = NULL, beta.hat = NULL, tol = 0.999999,
+    max_iters = 100, verbose = FALSE, optim.method = "Nelder-Mead",
+    optim.control = list(trace = 0, reltol = 1e-3, maxit = 5000),
+    parallel = FALSE, foldid = NULL) {
     model <- check_input(model, Y, X, locs)
     if (!is.null(beta.hat)) {
       if (!is.vector(beta.hat) | !is.numeric(beta.hat)) {
@@ -284,10 +284,7 @@ setMethod(
         parallel = parallel,
         foldid = foldid
       )
-      beta.hat <- as.matrix(predict(beta0.glmnet,
-        type = "coefficients",
-        s = beta0.glmnet$lambda.1se
-      ))
+      beta.hat <- as.matrix(predict(beta0.glmnet, type = "coefficients", s = beta0.glmnet$lambda.1se))
     }
     Y.hat <- beta.hat[1, 1] + model@X_train %*% beta.hat[-1, ]
 
diff --git a/R/PrestoGP_Util_Functions.R b/R/PrestoGP_Util_Functions.R
index 3213bb0..8e62898 100644
--- a/R/PrestoGP_Util_Functions.R
+++ b/R/PrestoGP_Util_Functions.R
@@ -150,7 +150,7 @@ transform_iid <- function(data, vecchia.approx, covparms, nuggets) {
 
   # return to original ordering
   orig.order <- order(U.obj$ord)
-  transformed.data <- transform.ord[orig.order, ] # TODO: @Eric.Bair is this a required variable?
+  transformed.data <- transform.ord[orig.order, ] # nolint # TODO: @Eric.Bair is this a required variable?
   return(transform.ord)
 }
 
@@ -180,7 +180,7 @@ transform_miid <- function(data, vecchia.approx, params) {
 
   # return to original ordering
   orig.order <- order(U.obj$ord)
-  transformed.data <- transform.ord[orig.order, ] # TODO: @Eric.Bair is this a required variable?
+  transformed.data <- transform.ord[orig.order, ] # nolint TODO: @Eric.Bair is this a required variable?
   return(transform.ord)
 }
 
diff --git a/R/PrestoGP_Vecchia.R b/R/PrestoGP_Vecchia.R
index eda0117..df66061 100755
--- a/R/PrestoGP_Vecchia.R
+++ b/R/PrestoGP_Vecchia.R
@@ -100,8 +100,8 @@ setMethod("prestogp_predict", "VecchiaModel",
     } else {
       warning("Variance estimates do not include model fitting variance and are anticonservative. Use with caution.")
       # TODO: @Eric.Bair is this a typo/bug? Capital 'V' in Vec.sds but 'vec.sds' in return.list
-      Vec.sds <- sqrt(pred$var.pred + model@covparams[4])
-      return.list <- list(means = Vec.mean, sds = vec.sds)
+      Vec.sds <- sqrt(pred$var.pred + model@covparams[4]) # nolint
+      return.list <- list(means = Vec.mean, sds = vec.sds) # nolint
     }
 
     return(return.list)