more cmdcheck fixes

Author: Nicholas Clark

R/add_residuals.R

@@ -1,6 +1,7 @@
 #'@title Calculate randomized quantile residuals for mvgam objects
 #'@name add_residuals.mvgam
 #'@param object \code{list} object returned from \code{mvgam}. See [mvgam()]
+#'@param ... unused
 #'@details For each series, randomized quantile (i.e. Dunn-Smyth) residuals are calculated for inspecting model diagnostics
 #'If the fitted model is appropriate then Dunn-Smyth residuals will be standard normal in distribution and no
 #'autocorrelation will be evident. When a particular observation is missing, the residual is calculated by comparing independent
@@ -14,7 +15,7 @@ add_residuals <- function(object, ...){
 #'@rdname add_residuals.mvgam
 #'@method add_residuals mvgam
 #'@export
-add_residuals.mvgam = function(object){
+add_residuals.mvgam = function(object, ...){
   resids <- dsresids_vec(object)
   object$resids <- resids
   return(object)

R/families.R

@@ -293,8 +293,10 @@ mvgam_predict = function(Xp,
             out[i] <- NA
           } else {
             ks <- truth[i]:cap[i]
-            lik_binom <- dbinom(truth[i], size = ks, p = p[i], log = TRUE)
-            lik_poisson <- dpois(x = ks, lambda = lambdas[i], log = TRUE)
+            lik_binom <- dbinom(truth[i], size = ks,
+                                prob = p[i], log = TRUE)
+            lik_poisson <- dpois(x = ks,
+                                 lambda = lambdas[i], log = TRUE)
             loglik <- lik_binom + lik_poisson
             out[i] <- log_sum_exp(loglik)
           }
@@ -317,8 +319,10 @@ mvgam_predict = function(Xp,
             out <- NA
           } else {
             ks <- min_cap[[i]]:cap[[i]]
-            lik <- exp(dbinom(truth[[i]], size = ks, p = p[[i]], log = TRUE) +
-                         dpois(x = ks, lambda = lambdas[[i]], log = TRUE))
+            lik <- exp(dbinom(truth[[i]], size = ks,
+                              prob = p[[i]], log = TRUE) +
+                         dpois(x = ks,
+                               lambda = lambdas[[i]], log = TRUE))
             probs <- lik / sum(lik)
             probs[!is.finite(probs)] <- 0
             out <- ks[wrswoR::sample_int_ccrank(length(ks),

R/globals.R

@@ -10,4 +10,4 @@ utils::globalVariables(c("y", "year", "smooth_vals", "smooth_num",
                          "term", "data_test", "object", "row_num", "trends_test",
                          "trend", "trend_series", "trend_y", ".", "gam",
                          "group", "mod", "row_id", "byvar", "direction",
-                         "index..time..index", "trend_test"))
+                         "index..time..index", "trend_test", "Var2"))